Old Fashioned Ecg Machine in China Image
Sensors (Basel). 2021 Sep; 21(18): 6036.
Anytime ECG Monitoring through the Apply of a Depression-Price, Convenient, Wearable Device
Carlo Massaroni, Bookish Editor, Emiliano Schena, Academic Editor, and Domenico Formica, Academic Editor
Received 2021 Jul 29; Accustomed 2021 Sep 6.
Abstract
Every yr cardiovascular diseases impale the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected equally country-of-the-fine art solutions, e.1000., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the marketplace. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In improver, the quality of recordings is another key factor to perform reliable diagnosis. The ECG Lookout man is a device designed for targeting all these issues. Information technology is inexpensive, habiliment (size of a watch), and tin be used without the demand for any medical expertise nigh positioning or usage. It is not-invasive, information technology records unmarried-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It tin can acquire whatsoever of the three peripheral leads; results can exist shared with physicians by simply tapping a smartphone app. The ECG Watch quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG Lookout man is a promising device for anytime cardiac health monitoring.
Keywords: analog filters, atrial fibrillation, ECG Watch, ECG, EKG, electrocardiogram, instrumentation amplifier, mobile healthcare, telemedicine
1. Introduction
The continuous pumping activeness of the heart is a fundamental need for homo life; indeed, through contractions and relaxations, i.e., the cardiac bicycle, it pumps the blood through the circulatory system vessels allowing the oxygenation of the trunk organs. This procedure is regulated by electrical impulses that stimulate different parts of the center and should repeat constantly. Natural human aging may lead to irregularities in the middle pace causing cardiovascular diseases (CVDs), such as arrhythmias, myocardial ischemia or infarction. CVDs remain the most mutual cause of expiry worldwide. Around one third of all deaths are related to this grouping of pathologies, more than twice that acquired past cancer, as well every bit more than all communicable, maternal, neonatal, and nutritional disorders combined [1,ii,3,4]. According to statistics, the size of the elderly population is expected to grow substantially in the next few years; as a issue, CVDs will follow the aforementioned trend [5]. In such a scenario, instrumentation and measurement represents a fundamental asset for cardiologists to sympathize patient atmospheric condition and perform diagnoses [half-dozen].
The standard process to analyze the eye's state of health is the utilise of a multi-lead electrocardiograph, which records heart electrical activity through wet electrodes placed on the skin and visualizes it into a time graph, called an electrocardiogram (ECG) [seven,8]. Generally, these machines perform high-resolution acquisitions and, for this reason, are quite expensive. ECG has been proven to exist the about efficient mode to diagnose CVDs [9,10,11]; indeed, the use of a multi-lead recording system provides, as output, a collection of signals, which represent different perspectives of the middle musculus's electrical field allowing physicians to have a comprehensive view of the patient heart. In guild to observe anomalies in the recorded ECG traces, both patients and doctors must be in the same room together with the electrocardiograph. Diseases characterized by sporadic events, such as the atrial fibrillation (A-fib), cannot exist diagnosed if they practice not occur exactly during ECG acquisition. Unfortunately, in a more realistic scenario, these pathologies may exist latent for a very long time and, in the worst case, tin can kill people without any evident symptoms.
To overcome these limitations, different strategies have been proposed [12,13]. The well-nigh adopted solution is the Holter device [xiv,fifteen,16,17] for the continuous monitoring (24 h–48 h) of CVD patients. In this way, cardiologists accept at their disposal plenty data to record even sporadic anomalies, and diagnosis could exist performed in a timely mode. Unfortunately, they are not-wireless and expensive, limiting the number of patients they could exist practical on; their recording autonomy is limited and, sometimes, not sufficient for discovering sporadic just very astringent diseases. Finally, they cannot be used for real-time diagnosis considering they need to record information and and then, after the device removal from the patient's body, ECG traces have to be inspected by a cardiologist.
In improver to devices already available in the market place, which will be analyzed in detail in the adjacent section, many proofs of concept have been proposed in literature for non-invasive, wearable, and reliable eye monitoring [eighteen,19,20,21,22]. A diagnostic ECG device exploiting web-services to share recordings is introduced in [23], while [24] uses disposable electrodes and a built-in warning system. Sensing devices for acquiring multiple vital signs are presented in [25,26,27] and [28] for CVD remote monitoring and electromyography/electrocardiography, respectively. Conductive textile is used in [29,xxx,31,32,33,34,35] to acquire ECG through sensors embedded in wearing apparel, while armband and multi-ring are employed in [36,37,38], respectively. Smartphone-based devices are proposed in [39,40,41,42,43,44]. ECG recording devices with particular focus on low power are shown in [45,46,47,48]. In these two latter categories, it is found the ECG Lookout [49,50,51], which can record one-lead ECGs in just 10 s and, as well, looks for silent atrial fibrillation. It is low price (~xxx €), wearable, slightly bigger (five cm × three cm × i.5 cm) than an everyday watch, wireless, and information technology does not crave cables or disposable electrodes. Acquisitions are transmitted via Bluetooth to a custom smartphone app, where the recorded ECG is filtered to remove acquisition dissonance and baseline wandering. Then, the resulting point is visualized and it can be shared via post upon user request. Moreover, while parsing the ECG record, the embedded algorithm is able to automatically detect and bespeak atrial fibrillation episode. Finally, since information technology is a wearable device, it should besides ensure, as much as possible, condolement when worn [xviii].
This newspaper presents an extension of [49], where the authors provided a full general overview of the ECG WATCH and the initial experimental results. Here, the last version of the device, shown in Effigy 1, is introduced and fully described with respect to its design specifications. Since information technology is a wearable device, its power consumption over time was fully assessed, and its accurateness was compared with the gold standard, both from visual and analytic perspectives. Finally, the algorithm for automatic atrial fibrillation detection was detailed and tested confronting a certified CVD simulator.
Department 2 presents state-of-the-art, commercially available, wearable devices for ECG recording. Section iii describes the ECG Sentinel, which is then compared with a certified electrocardiograph in Section 4. Finally, Department 5 yields the conclusions.
2. Country of the Fine art
Portable devices for ECG acquisition are already bachelor in the market [52], but only some of them can be used for both enquiry and medical purposes with fifty-fifty less allowing the user to share results (eastward.g., via mail), and none of them provide the ECG trace in a numerical way. The majority of them are not wear and their recording time is typically greater than 20 south, which makes them highly decumbent to muscular dissonance since acquisitions are not performed on the bed. An instance is [53], which tin learn, ane by ane, the 3 peripheral leads (i.e., lead I, lead 2, atomic number 82 Iii), but information technology cannot print or share the recorded ECG. Furthermore, the quality of its recordings is not sufficient to be exploited from physicians to brand a proper diagnosis.
A fast-growing class of devices is due east-wellness, which are able to monitor and record several vital parameters of an individual, e.g., heart rate or claret oxygen level, using specialized hardware. E-health devices can exist continued, e.chiliad., via Bluetooth, to a smartphone, which can exist exploited for user interface, data store, real-time analysis, network uplink, and its onboard sensors, such every bit accelerometers, cameras, and GPS [54].
The AliveCor [55,56], which is present both in the American and the European market (nether unlike brands), can learn a xxx s single-lead ECG and transmit it through an audio channel to the associated app. Then, the indicate is processed to remove racket and search for any anomalies. It is a two-electrode device to be fastened to the backside of the smartphone. Indeed, because of the called channel (i.e., audio transmission), information technology needs to operate very shut to the phone. It is FDA approved and CE marked as a medical device. The output is a PDF file that tin be shared past mail; as before, no numerical point is attainable.
A similar device is [57], which uses Bluetooth protocol to transmit data instead of an audio protocol. It is FDA cleared and U.S. citizens can subscribe to a professional service that will check 30 s ECG recordings remotely. The output is a PDF file that can be shared past mail; no numerical signal is accessible.
Instead of a two electrodes bar to be used between two hands, QardioCore [58] proposes a breast belt to be worn under the clothes. The advantage of this approach is the possibility of a continuous ECG. It can as well monitor a person'southward concrete activity and perspiration charge per unit. It is FDA canonical, CE marked, and clinically validated as a medical device. Although it is designed for endless conquering, its design is better suited for usage during sport. Being worn constantly under clothes during everyday activities may result as uncomfortable. Finally, it works only with Apple iPhones.
The latter version of the Apple Watch [59] performs both centre charge per unit computation and ECG recording using electrodes on its dorsum and on the clock band. Acquisitions can be stored into the spotter or shared with Apple smartphones. Information technology is wearable, wireless, and does not crave any medical expertise to correctly learn an ECG. Its thirty s ECG recording is FDA compliant. It only provides PDF files while ECG numerical trace is not accessible.
To conclude, to the best of our noesis, all available solutions need a long recording fourth dimension (not less than 20 s), which is acceptable in stable situations, such as in an infirmary bed, but more than likely to be corrupted with noise during mundane activities. Furthermore, though it is undeniable—from a statistical point of view—that a paroxysmal atrial fibrillation with a few arrhythmias during the solar day can exist better detected with long-fourth dimension measurements, it is as well true that a severe atrial fibrillation, typical of severe patients, is a continuous outcome. Thus, it is e'er detected in ten s. Conversely, the ECG WATCH requires half of the acquisition time, in the worst-case scenario, with regards to other technologies. Moreover, as a side upshot, shorter acquisitions too mean that it is less probable to decadent the measurement with involuntary movements (i.east., muscle noise). Finally, different the available solutions, the ECG WATCH besides provides results in a numerical format (besides a graphical PDF representation of the indicate), which can be exploited for research activities and deeper medical assay of the ECG traces (e.m., using different filtering or performing automated analysis with rhythm recognition algorithms).
3. The ECG Scout
The ECG WATCH is a wearable, wireless, non-invasive device designed and built by the authors in the Neuronica Lab of the Politecnico di Torino. It is a heart monitor for hands recording 10 southward unmarried-atomic number 82 ECG and visualizing it into a smartphone or desktop app. The recording can be sent to a doc who tin can analyze information technology and determine if the subject area requires a deeper exam (see Figure two).
ECG visualization: mobile app (left); physician desktop software (right).
The ECG Watch is capable of acquiring skin biopotential through two electrodes: one placed on the front end of the device, and one on the back. By touching the electrodes with different parts of the torso (according to the Einthoven'due south triangle [threescore]), information technology is possible to acquire 1 of the 3 ECG's primary derivations. Having a watch class gene means that, in normal operations, it is worn on the wrist. Therefore, the start electrode is in contact with one of the wrists, while the other one is complimentary to exist touched with the other hand, resulting in a lead I acquisition; or with the contrary leg, resulting in lead II or III conquering (depending on whether the sentry is worn on the left or right side).
The acquisition only lasts ten s, and after that it is elaborated and transmitted via Bluetooth to the smartphone app. The application is the main way to interact with the acquired signals. Among various functions, it is mainly used for: filtering the signals coming from the device; storing the acquisitions in a database; displaying the signals; and sharing the acquisitions via email. Moreover, the application has a congenital-in algorithm for atrial fibrillation recognition that monitors every acquisition sending an alarm in the instance of an episode.
Effigy 3 represents the block diagram of the ECG Sentinel. A microcontroller powered by a LiPo battery is capable of acquiring an ECG signal through an analog front-end and transmitting the acquired information through a Bluetooth module. The next department volition address the various elements in more detail.
ECG Picket block diagram: ADC—Analog-to-Digital Converter; GPIO—General Purpose I/O pins.
iii.1. Analog Circuit Design
The circuit used in the ECG Watch (encounter Effigy 4) takes inspiration past Thakhor and Webster [61] with the main difference of keeping particular attending on a lower power consumption and a smaller area. The expert quality of the integrated circuit (IC) used in the front end end, given by very loftier CMRR (Mutual Mode Rejection Ratio) and PSRR (Power Supply Rejection Ratio), makes the utilize of a right leg drive amplifier unnecessary [62]; albeit it could still be implemented for a less portable awarding because of the versatility of the circuit.
Analog chain. From left to correct, the highlighted boxes contain: the electrodes, the passive high-pass filter, the differential amplifier, and the agile band-pass filter.
The analog front end-stop (run into Figure 4) consists of a passive high-pass filter that feeds an instrumentation-amplifier (Texas Instruments, INA333), followed by an active ring-pass filter. The acquisition chain ends with the microcontroller ADC. The instrumentation-amplifier is set with a gain of forty dB, whilst the active band-laissez passer has a 20 dB gain, for a thou full of lx dB gain in the immune band. Since most of the ECG spectrum is located below seventy Hz [63], the band-pass filter has been designed with a band of [0.seven Hz–72 Hz] with the following transfer function:
(1)
which has a zero in the origin, two poles at −1/R 1 C 1 and −i/R 2 C ii, and a proceeds of −R 2/R 1. During the design phase, it was also taken into consideration to add a l Hz notch-filter in lodge to further eliminate the noise from the main-line coupling, just the results were already satisfying as they were.
The whole excursion is powered from a single 120 mAh LiPo bombardment that is regulated at 3.3 Five by a buck/boost switching regulator operating at a relatively high frequency compared to the other signals. The 3.iii V is split in half (1.65 Five) with a voltage divider coupled with a voltage follower OpAmp and used as a reference voltage. This reference is used equally a virtual footing for the indicate coming out of the INA333, so to avoid a double-concluded ability supply.
Finally, the front-cease is connected to the patient through 2 small (two × 2 × 0.1 cm) stainless steel electrodes. This material offers the best compromise in terms of costs, usability, indicate stability, and mechanical resistance.
In Figure 5 and Effigy 6, the analog front-end is all full-bodied in the extreme left of the board, occupying but 90 mm2 of board space (not including the electrodes connector).
The mounted PCB: top (left) and bottom (right).
The PCB Gerber view: top (left) and bottom (right).
3.2. Digital Circuit Pattern
The ECG betoken is sampled at 1 kbps past the TI MSP430 microcontroller (µC) which has a x b 200 kbps SAR ADC on board, whose voltage reference is provided past an external component.
X seconds of recording are sufficient to the app embedded algorithm for assessing the risk of an atrial fibrillation; still, the µC flash retention is big enough to memorize on board a k total of 70 southward of ECG sampled at 1 kbps, removing the need of an additional memory unit and, therefore, saving some space on the board.
Since the application is not time disquisitional, to further reduce the printed circuit lath (PCB) dimensions, the µC works at 16 MHz using its internal oscillator. The µC computational power is far beyond the actual needs of the application. As a event, some digital bespeak processing could be performed directly on board [64] and it is actually under study. Figure five and Figure 6 yield the PCB: on the bottom (meet Figure 5 and Effigy 6 correct), there are the connectors for the battery, the two electrodes, and the USB recharger; while the tiptop, shown in Figure 5 and Effigy six left, houses the µC and other components of the front end.
3.3. Power Consumption
Well-nigh of the power consumption depends on the digital and ability circuits, which grossly absorbs xxx mW for the recording and 150 mW for the cursory Bluetooth data transmission. The analog excursion only draws approximately one.5 mW due to the extremely low-power OpAmp and InAmp employed in the pattern. In fact, the chosen ICs (Texas Instruments OPA4330 and INA333) combine very depression power consumption with very high performances in terms of PSRR, CMRR (for the INA333), offset, drift, noise, and an internal EMI filter. Using a standard 120 mAh single-cell LiPo battery ECG WATCH has an estimated bombardment life of 8 days, assuming a heavy use of fifty acquisitions per mean solar day.
Finally, the user tin can recharge the device with a standard USB blazon micro-B cable, vastly employed for charging smartphones and, therefore, widely bachelor.
4. Experiments
To appraise the quality of ECG Spotter acquisitions, a comparative written report with a standard three-lead patient monitor was conducted on thirty people (15 males, 15 females) anile 25–35 years old with no cardiac issues, and with a patient simulator (Fluke Biomedical ProSim 4). The appliance called for the comparing is the GE Healthcare patient monitor B105, a CE medical device employed by doctors in hospitals or infirmaries.
Three channels, four electrodes, ECG recordings were taken using pre-gelled Argent-Silver Chloride (Ag/AgCl) electrodes as standard for ECGs comparison in the PolitoBIO Med laboratory of Politecnico di Torino. ECG Sentry acquisitions were taken among wrists, except in five cases (ii males, 3 females), where lead I'south signal was too weak (not clearly visible) and acquisitions were taken betwixt the right arm and the left leg, i.e., lead II. The choice of acquiring lead II's bespeak was decided past the research squad. The associated procedure will exist detailed in the instruction manual. As post-processing three different filters were used: a baseline-wander removal filter, a notch filter used to remove 50 Hz noise, and low-pass moving average filter to smoothen the results. The acquisitions were taken simultaneously. And so, the ECG WATCH and B105 recordings were manually aligned using time as a reference.
Figure 7 compares the recordings on a single subject area of the ECG Lookout (blue) and the B105 (green). Qualitatively speaking, it tin can exist stated that the ECG Sentinel conquering is quite similar to the aureate standard one. This is, of course, just a qualitative example of the accuracy of the ECG watch compared to B105, and the rest of this section will focus more on the quantitative aspect. In this sense, the ECG Watch aims to be considered as a medical device and, in fact, the CE medical certification procedure has already begun.
Comparison between ECG WATCH (top) and GE Healthcare B105 (lesser) on a single subject pb I.
four.1. Bland-Altman Plot
The center rate ciphering has been the outset criterion for evaluating the ECG Scout quality with respect to the GE Healthcare B105. The difference betwixt the 2 devices was adamant with the Bland-Altman plot (BA plot) [65] shown in Figure 8: each blue point represents a pair of heart rate calculations and provides an estimation of the discrepancy betwixt the ii devices for a given simultaneous acquisition; differences between couples of measurements are plotted in ordinates, while their respective ways are drawn in the abscissa. The BA plot avoids uncertainties of different measurements because the estimation is evaluated on the differences of a unmarried couple of acquisitions; thus, recording conditions (e.thousand., heart charge per unit variability) do not interfere with the results. In this sense, the BA plot is a descriptive statistical tool for comparing two devices.
Banal-Altman plot for the GE Healthcare B105 and the ECG Lookout man: each blue point represents a pair of center charge per unit calculations; xanthous line represents the average difference, while orange and green lines stand for the lower and upper bounds of the 5% fiducial interval.
From Effigy viii, it clearly stems out measurements are biased of simply 0.vi heartbeat (HB) per minute, which means ECG WATCH overestimates, on average, the HB by 0.6 bpm. Yet, data are consistent because they vary in an interval of less than 5% of the mean value. Furthermore, the cross correlation betwixt the two centre rate estimations is effectually 98.7% with an average standard divergence for each patient of 2 bpm. As a result, the ECG Picket can be considered as a valid tool to compute heart rate, which is i of the fundamental parameters monitored by cardiologists to assess the cardiac state of health.
iv.2. Power Spectral Density (PSD)
The ECG WATCH is non merely a heart charge per unit monitor but mainly an ECG recorder; therefore, the second benchmark is the ECG quality. As explained in [49], despite the amount of instrumentation and knowledge on ECG, asserting its quality is not picayune, peculiarly from an analytical perspective. Hither, the ECG WATCH acquired indicate was evaluated both in the frequency and time domains.
The ability spectral density (PSD) provides information on the bespeak power distribution amid the spectrum; in this sense, it can be considered as the information content of each frequency. For the sake of simplicity, among the different techniques for estimating a signal'due south PSD, the squared detached fast Fourier transform (FFT) module has been employed:
(ii)
Figure 9 yields the comparison between the PSDs for the ECG Sentry and the B105.
Power spectral density comparison in the band 0–40 Hz for the GE Healthcare B105 (blue) and the ECG WATCH (red).
Despite in that location being no visual significant divergence between the two densities, an additional analytical study was performed using cumulative spectral power (CSP), which is derived from PSD as a cumulative sum normalized with the full power. The resulting curve, called CSP(f), is a monotone function to represent the percentage of energy encompassed in the spectrum within a specific frequency f:
The argument f can exist exploited to derive at which frequency the signal reaches a certain corporeality of the full power and thus of the information content. In this sense, it tin be defined as the median, i.e., the frequency, that splits the ability in half, and a bandwidth around it, which has been set to sixty%. The CSP formula has been practical to 90 acquisitions taken from thirty bearding volunteers (three per patient), and 30 acquisitions taken using ProSim4 simulating both normal sinus rhythm and atrial fibrillation. All acquisitions have been performed at the same time for both ECG Watch and B105 to have consequent results. In order to obtain an thought of the short-term differences between the devices, Table 1 contains the comparative results for some of the acquisitions.
Table 1
Comparison between ECG Lookout man and B105 cumulative Spectrum Power frequencies for paired acquisitions.
| ECG Scout f 20% [Hz] | B105 f 20% [Hz] | ECG Scout f 50% [Hz] | B105 f 50% [Hz] | ECG WATCH f 80% [Hz] | B105 f lxxx% [Hz] |
|---|---|---|---|---|---|
| 3.4 | 3.4 | 9.8 | nine.8 | 17.0 | 17.i |
| 1.7 | 2.5 | 7.6 | 7.8 | 15.7 | 15.nine |
| 3.two | three.6 | 8.2 | vii.vii | xiv.6 | xiii.9 |
| 4.vi | 4.two | 10.1 | 9.7 | 17.5 | 17.seven |
| 4.5 | four.ii | 8.8 | 7.5 | 15.5 | 15.2 |
| 1.vi | i.2 | 4.0 | 3.7 | 12.9 | 12.4 |
| 4.3 | three.viii | 9.two | 9.0 | 16.6 | 17.1 |
| 3.2 | 3.2 | 9.7 | ix.8 | sixteen.8 | 16.7 |
| 3.6 | 3.6 | 10.5 | 8.vi | sixteen.i | xiv.6 |
| 3.eight | 1.9 | 9.i | vii.3 | xvi.four | 15.2 |
Withal, to have a better understanding of the organization performance in terms of multiple acquisitions, Tabular array ii shows the average values of the frequencies at which 20%, 50%, and 80% of total power is distributed, according to CSP.
Table 2
Cumulative spectrum power frequencies.
| System | f xx% [Hz] | f 50% [Hz] | f 80% [Hz] |
|---|---|---|---|
| B105 | 3.9 | 8.7 | 15.three |
| ECG WATCH | 3.vi | 8.vi | 15.iii |
The values in Table 2 validate the information content of the ECG Scout and B105 is distributed in a similar way, in co-ordinate with Effigy 9: the ECG Scout has a spectrum concentrated on slightly lower frequencies than the B105, where the nifty office of the ECG data is located [66].
four.3. Signal to Noise Ratio (SNR)
Another means of evaluation based on the frequency domain is the betoken to noise ratio (SNR). Information technology is defined as the ratio of signal power to the noise power, and it is normally expressed in decibel (dB):
By definition, signal and noise are the meaningful and the unmeaningful data, respectively, and are chosen arbitrarily depending on the system to evaluate.
For this comparison, it has been defined as signal, i.e., meaningful information, everything in the bandwidth of 0.67–40 Hz as stated in IEC 60601-2-27 regarding electrocardiographic monitoring instruments, and noise as everything lying outside that frequency ring. Thus, Psignal and Pnoise , in Equation (4), are referred to the sum of the power spectral density (Equation (ii)) evaluated inside and outside the significative bandwidth [0.67–40] Hz, respectively.
The results are summarized as hateful and standard deviation in Table iii. The same considerations regarding the data in Table 2 also apply for Table 3.
Table 3
Point to dissonance ratio (SNR).
| Mean [dB] | Standard Deviation [dB] | |
|---|---|---|
| B105 | 145.7 | 27 |
| ECG WATCH | 128.xiv | 10 |
Albeit, the results show that the ECG Lookout man has a slightly lower SNR than the B105, information technology has less variability, which means the information content of its acquisitions is more consistent in the considered bandwidth. Moreover, a difference of 17 dB on average is not very significant when the values are way in a higher place 100 dB.
4.iv. Fourth dimension-Domain Differences
The final comparison between the ECG WATCH and B105 was performed in the time domain. To this purpose, a dataset fabricated of single HBs randomly extracted from volunteers was used for evaluating betoken-to-point discrepancies of the two measurement devices. The contemporary acquired signals from the B105 and ECG Sentinel were first normalized, and then matching HBs were isolated and compared in pairs. To better explicate the concept, Figure 10 shows an case of such couple of heartbeats: one acquired with the ECG Sentry (in red), and the other one with the B105 (in blue).
Unmarried heartbeat isolated from ECG WATCH (reddish) and GE Healthcare B105 (bluish) contemporary acquisition.
Table four reports the average, the standard deviation, and the maximum value of the difference between each point of the signals normalized to 1: it can be observed that there are not significative differences, with a mean value below three%, and a standard deviation around 9%.
Table 4
Time domain differences.
| Hateful | Standard Deviation | Max | |
|---|---|---|---|
| Differences | −0.027 | 0.0931 | 0.1508 |
5. Atrial Fibrillation Detection
One of the nigh frequent, dangerous, and hard to detect cardiac pathologies is atrial fibrillation. According to [67], A-fib is an aberrant heart rhythm where heart atrial chambers trounce with a rapid and irregular footstep. Information technology can remain silent, i.eastward., without any symptoms [68], for years and undetected even by professional tools. Indeed, it frequently begins equally a few aberrant beatings which get more frequent over time [69]. Occasionally there may be symptoms, such as heart palpitations, fainting, lightheadedness, shortness of breath, or chest pain [70]. Furthermore, a eye chirapsia in such an irregular fashion increases the risk of heart failure, dementia, and stroke [67].
The ECG WATCH is sized as pocket-sized as a watch, to be worn on wrist, and needs merely a tap on a phone app to tape a 10 s ECG, that is, to check cardiac wellness. Information technology does not require any detail expertise, e.k., medical, to be used; therefore, the ECG WATCH is perfectly suitable to perform a heart check anytime, anywhere. For this purpose, the app embeds an algorithm for automatically detecting an atrial fibrillation (run across Figure eleven). At first, the R peaks, i.e., the heartbeats, are extracted from the ten s recorded ECG using the well-known Pan–Tompkins algorithm [71]. Then, both the beat-by-beat and overall rhythm are analyzed to check if their variations over time exceed the predefined thresholds (experiments showed that a good value is around iii bpm); if so, the recording is classified as A-fib. On the contrary, if the rhythm is considered as "normal", a last check on the P wave is performed. As is well known in medicine, in the case of atrial fibrillation, P waves volition be absent-minded. However, some people with A-fib will take fibrillatory waves, i.e., a wavy baseline, on their ECG, which bespeak atria pulse irregularly. They may resemble P waves, and this can make an A-fib rhythm look like a sinus 1. The final block of the algorithm looks for P waves by ways of the highest maxima before the R-height. When it found a wave resembling a P wave, its amplitude, elapsing, and distance from previous and subsequent QRS complexes are checked to decide if information technology is a true P wave or a fibrillatory one.
A-fib detection algorithm: block diagram.
Algorithm Assessment
The A-fib algorithm has been tested both on existent and imitation recordings. Figure 12 shows some examples of x s arrhythmic ECGs taken from existent subjects: the illness is ever correctly recognized and signaled (see the pop-upward letters) past the desktop software. In this sense, information technology tin be stated that in the case of astringent A-fib, x seconds are sufficient for detecting this pathology and the ECG WATCH proves to be a valid tool for heart monitoring.
A-fib detection algorithm: real subject examples. The ECG Lookout desktop software analyzes the recording and automatically detects and signals the A-fib (see popup messages).
In order to assess the algorithm quality, a stress test was performed with the employ of a certified standard simulator, the Fluke Biomedical ProSim four, which is able to produce, among the others, both healthy and atrial fibrillation ECG signals. Effigy thirteen shows some examples: either fibroid (Effigy 13a,c,east) or fine (Figure xiiib,d,f) A-fibs were tested. The algorithm was able to correctly identify all the pathological traces as dangerous ones; thus, the mobile app generates an alert for the users by ways of a yellow triangle on the elevation left corner and of an audio-visual alert signal.
Fluke Biomedical ProSim 4 simulated ECGs: coarse (left) and fine (right) A-fibs. The ECG Sentinel mobile app signals the detected anomaly past means of a xanthous triangle on the top left corner and of an audio-visual warning indicate.
Finally, Figure 14 compares the ECG Scout (in red) and the GE Healthcare B105 (in bluish) acquisitions on a simulated atrial fibrillation betoken. As the previous example, ECG Scout recording is compatible with the GE Healthcare B105 one.
Fluke Biomedical ProSim 4 fake ECGs. Comparing betwixt GE Healthcare B105 (bluish), and ECG WATCH (reddish) recordings.
half dozen. Conclusions
Cardiovascular diseases characterized by occasional ECG anomalies, like atrial fibrillation, are difficult to be detected. Current solutions such as Holter or wearable devices neglect to properly tackle these pathologies. Indeed, despite they can notice some episodes, they are either non wearable or wireless and are often unsuitable for a long-time usage. In addition, the quality of recordings is some other key factor to perform reliable CVD diagnosis. The ECG WATCH is designed to solve all the above-mentioned issues at the same time; it is a depression-cost, clothing, wireless, unobtrusive device for acquiring ECG in only 10 s, someday, anywhere. It can acquire whatever of the peripheral leads and ship the recordings to doctors by just tapping a button on a smartphone app. It does not require whatever medical expertise to be positioned or used. The quality of proposed tool has been successfully assessed on xxx people with respect to a certified electrocardiograph. Furthermore, the ECG Picket requires at to the lowest degree half the acquisition time of other commercially available tools, and its numerical output can and so be exploited past a cardiologist for deeper inspection and analysis, equally it was shown in the experimental department. The app embeds an algorithm for A-fib detection, which was successfully tested with a certified ECG simulator on different severities of the pathology. Finally, the proposed device is also low-cost, which allows its adoption on a very large population.
In conclusion, the ECG Lookout has proved to be an interesting and promising device for someday cardiac health monitoring and for detecting silent atrial fibrillation without the need for medical expertise or going to a doctor. Time to come works will deal with device size reduction and an extension of the embedded algorithm for detecting more than pathologies.
7. Patents
The device presented here is based on patent WO2018073847A1: wearable device for acquiring electrocardiographic signals (ECG) signals.
Acknowledgments
A special thank you to Federico Caffarelli, Alessia Mauro, and Elisa Valli.
Author Contributions
The authors take contributed in the following way: Conceptualization: Due east.P. and 5.R.; Methodology: Eastward.P. and J.F.; Software: V.R.; Validation: V.R., J.F. and Eastward.P.; Formal analysis: J.F.; Investigation: J.F. and V.R.; Resources: Eastward.P. and V.R.; Data curation: J.F. and Five.R.; Writing—original draft preparation: Five.R. and J.F.; Writing—review and editing: E.P., J.F. and V.R.; Visualization: E.P.; Supervision: E.P.; Projection assistants: E.P. and V.R.; Funding conquering: E.P. and V.R. All authors have read and agreed to the published version of the manuscript.
Funding
This piece of work has been partly supported by the PoliToBIOMed Lab—Biomedical Engineering Lab of the Politecnico di Torino.
Institutional Review Board Statement
The study was conducted according to the guidelines of the Annunciation of Helsinki, and approved past the Institutional Review Board of the Neuronica Lab of the Politecnico di Torino (Protocol code: NN-EPA/2019/1012, appointment of approval: 31 March 2019).
Informed Consent Argument
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this report are bachelor on asking from the corresponding writer.
Conflicts of Involvement
The authors declare no disharmonize of interest.
Footnotes
Publisher'southward Notation: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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