From Pacemaker to Wearable: Techniques for ECG Detection Systems

Secure hardware IP of GLRT cascade using color interval graph based embedded fingerprint for ECG detector

This paper presents a security aware design methodology to design secure generalized likelihood ratio test (GLRT) hardware intellectual property (IP) core for electrocardiogram (ECG) detector against IP piracy and fraudulent claim of IP ownership threats. Integrating authentic (secure version) GLRT hardware IP core in the system-on-chip (SoC) of ECG detectors is paramount for reliable operation and estimation of ECG parametric data, such as Q wave, R wave and S wave (QRS) complex detection. A pirated GLRT hardware IP integrated into an ECG detector may result in an unreliable/erratic estimation of ECG parametric data that can be hazardous and fatal for the end patient. The proposed methodology presents an integrated design flow to secure micro GLRT and GLRT cascade hardware IP cores for the ECG detector, using the colored interval graph (CIG) framework based fingerprint biometric, during high level synthesis (HLS). The proposed approach integrates a fingerprint biometric based security constraint generation process for securing the GLRT hardware IP core. This paper also presents a secure register transfer level (RTL) datapath design corresponding to micro GLRT and GLRT cascade hardware IP cores with embedded IP vendor's fingerprint. The proposed secure GLRT hardware IP core embedded with fingerprint biometric achieves superior results in terms of probability of coincidence and tamper tolerance than other security approaches. More explicitly, the proposed approach reports a significantly lower value of probability of coincidence and stronger value for tamper tolerance. Further, the proposed approach incurs zero design cost overhead.

Algorithm for Mobile Platform-Based Real-Time QRS Detection

Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan-Tompkins (AMPT), which is a simplified version of the well-established Pan-Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan-Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5-20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection.

Comparative study of a single lead ECG in a wearable device

BACKGROUND

Technological advances have led to electrocardiograph (ECG) functionality becoming increasingly accessible in wearable health devices, which has the potential to vastly expand the clinician's ability to monitor, diagnose, and manage cardiac health conditions. However, achieving the high signal quality necessary to make an accurate and confident diagnosis is inherently challenging on consumer device-acquired ECGs. Effective signal conditioning is crucial to make ECG data from wearable devices clinically actionable.

OBJECTIVE

This study evaluates the heart rate (HR) performance of ECG data collected on the HeartKey® Test Watch, a single lead, dry electrode wrist wearable, against data acquired on two criterion devices: the Bittium® Faros 180, a gold standard wet electrode ambulatory monitoring device, and the HeartKey Chest Module.

METHODS

ECG data was simultaneously acquired on three devices during a multi-stage protocol (sitting, walking, standing) designed to reflect the motion noise of real-life scenarios. Raw ECGs from the HeartKey Test Watch and HeartKey Chest Module were processed through HeartKey software, and the accuracy of the outputted heart rate data was compared to that of the criterion device at each stage of the protocol. A beat rejection analysis was performed to provide insight into the degree of high-frequency noise present in ECGs recorded on the HeartKey Test Watch.

RESULTS

Data acquired on the HeartKey Test Watch and processed by HeartKey software generated HR metrics that closely matched that of the criterion devices throughout the protocol. Bland-Altman analysis showed a mean absolute HR difference of 0.74, 1.21, 0.80 bpm during the sitting, walking, and standing stages respectively, which is within the ± 10% or ±5 bpm range required by ANSI EC13. ECG data from the HeartKey Test Watch had a higher beat rejection rate relative to the HeartKey Chest Module (8.5% vs ∼0%) due to the excessive high-frequency noise generated during the motion-based protocol.

CONCLUSION

HeartKey software demonstrated highly accurate HR performance, comparable to that of the criterion Faros device, when processing challenging ECG data acquired on a single lead, dry electrode wrist wearable during both non-motion and motion-based protocols.

Basis pursuit sparse decomposition using tunable-Q wavelet transform (BPSD-TQWT) for denoising of electrocardiograms

The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in an ECG acquisition unit is noise interference. Essentially, the prominent ECG noise sources are power line interference (PLI) and Baseline drift (BD). Therefore, in the study, a new technique called the basis pursuit sparse decomposition (BPSD) using tunable-Q wavelet transform (TQWT) is proposed to remove the PLI and BD present in the ECG recordings. Chiefly, the TQWT method is a wavelet transform with distinct Quality factors (Q) which can adjust the signal to the natural non-stationary behaviour in time and space. Further, the method decomposes the signal into high-Quality factor and low-Quality factor components of wavelet coefficients to eliminate PLI and BD by choosing appropriate redundancy (r) and decomposition levels (J2). The 'r' and 'J' values are chosen based on the trial-and-error method concerning signal-to-noise ratio (SNR). It has been found that the PLI noise has been suppressed significantly with the redundancy of 3 and decomposition levels of 10; more so, the BD has been removed with the redundancy of 4 and decomposition levels of 19. The proposed method BPSD-TQWT was evaluated using the open-source MIT-BIH Arrhythmia database and the real-time ECG recordings collected through a wearable Silver Plated Nylon Woven (Ag-NyW) textile-based ECG monitoring system. The performance was then evaluated using fidelity metrics such as SNR, maximum absolute error (MAX), and normalized cross-correlation coefficient (NCC). The results were compared with IIR filter, stationary wavelet transform (SWT), non-local means (NLM) and local means (LM) methods. Using the proposed method on MIT-BIH Arrhythmia Database, performance evaluation parameters such as SNR, MAX, and NCC were improved by 4.3 dB and 6.8 dB, 0.37 and 0.78, 0.2 and 0.46 compared to IIR and SWT methods respectively. On the other hand, using the proposed method on the real-time datasets, values of SNR, MAX, and NCC were improved by 0.3 dB and 0.6 dB, 0.009 and 0.74 and 0.3 and 0.35 compared to IIR and SWT methods respectively. Finally, it can be concluded that the proposed method shows improved performance over IIR, SWT, NLM and LM methods for PLI and BD removal.

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

Reliable P wave detection in pathological ECG signals

Accurate automated detection of P waves in ECG allows to provide fast correct diagnosis of various cardiac arrhythmias and select suitable strategy for patients’ treatment. However, P waves detection is a still challenging task, especially in long-term ECGs with manifested cardiac pathologies. Software tools used in medical practice usually fail to detect P waves under pathological conditions. Most of recently published approaches have not been tested on such the signals at all. Here we introduce a novel method for accurate and reliable P wave detection, which is success in both normal and pathological cases. Our method uses phasor transform of ECG and innovative decision rules in order to improve P waves detection in pathological signals. The rules are based on a deep knowledge of heart manifestation during various arrhythmias, such as atrial fibrillation, premature ventricular contraction, etc. By involving the rules into the decision process, we are able to find the P wave in the correct location or, alternatively, not to search for it at all. In contrast to another studies, we use three, highly variable annotated ECG databases, which contain both normal and pathological records, to objectively validate our algorithm. The results for physiological records are Se = 98.56% and PP = 99.82% for MIT-BIH Arrhythmia Database (MITDP, with MITDB P-Wave Annotations) and Se = 99.23% and PP = 99.12% for QT database. These results are comparable with other published methods. For pathological signals, the proposed method reaches Se = 96.40% and PP = 91.56% for MITDB and Se = 93.07% and PP = 88.60% for Brno University of Technology ECG Signal Database with Annotations of P wave (BUT PDB). In these signals, the proposed detector greatly outperforms other methods and, thus, represents a huge step towards effective use of fully automated ECG analysis in a real medical practice.

Advances in Soft and Dry Electrodes for Wearable Health Monitoring Devices

Electrophysiology signals are crucial health status indicators as they are related to all human activities. Current demands for mobile healthcare have driven considerable interest in developing skin-mounted electrodes for health monitoring. Silver-Silver chloride-based (Ag-/AgCl) wet electrodes, commonly used in conventional clinical practice, provide excellent signal quality, but cannot monitor long-term signals due to gel evaporation and skin irritation. Therefore, the focus has shifted to developing dry electrodes that can operate without gels and extra adhesives. Compared to conventional wet electrodes, dry ones offer various advantages in terms of ease of use, long-term stability, and biocompatibility. This review outlines a systematic summary of the latest research on high-performance soft and dry electrodes. In addition, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, strategies to promote physical adhesion, methods for higher breathability, and their applications in wearable biomedical devices. Finally, we discuss the developmental challenges and advantages of various dry electrodes, while suggesting research directions for future studies.

Stationary wavelet transform based ECG signal denoising method

Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.

Developing a smart walking cane with remote electrocardiogram and fall detection

This study developed a smart cane with remote electrocardiogram (ECG) and fall detection. The cane comprises a self-developed ECG detection circuit, fall detection module composed of a three-axis gyroscope and three-axis accelerator, and two wireless transmission modules. The data reception end features a human–machine interface with self-developed ECG analysis and fall detection programs, providing reference data for identifying an abnormal situation. The hardware of the proposed system is divided into two parts. First, ECG detection is achieved using a copper column-shaped detector in place of conventional ECG electrodes. The self-developed sensor circuit amplifies the collected signals and filters unwanted noise to generate complete ECG signals. An Arduino MEGA microcontroller board and the two wireless transmission modules then transmit the signals to the human–machine interface. Second, fall detection is achieved using the aforementioned fall detection module to collect numerical data, which are then transmitted to the human–machine interface through the Arduino MEGA and wireless transmission modules. The proposed system can be applied to real-time monitoring and provide reference data for health care professionals and nursing personnel.

Area efficient folded undecimator based ECG detector

This paper presents an area-efficient folded wavelet filter-based Electrocardiogram (ECG) detector for cardiac pacemakers. The modified folded undecimator based detector consists of Wavelet Filter Bank, QRS complex detector with Generalized Likelihood Ratio Test (GLRT) block and noise detector. A high-level transformation technique such as folding transformation and Cutset retiming are applied to the GLRT block in order to reduce the silicon area. Folding is a high-level transformation applied at the architectural level to enhance the performance of DSP architectures. It reduces the number of adders, multipliers and delay elements in the architecture. The Cutset retiming reduces clock period of the architecture by changing position of delay elements in the critical path. The folding transformation and cutset retiming implement the functional blocks of the GLRT circuit with minimum hardware. The modified folded ECG detector is tested for short term and long-term MIT-BIH databases. The results show that the modified folded undecimator detector has hardware savings and achieves sensitivity of 99.95%, positive prediction of 99.97% and Detection Error Rate (DER) of 0.061. The folded GLRT block architecture is synthesized with FPGA Zed board XC7Z010CLG484-1. Results show that the device utilization and power consumption are lesser than the conventional GLRT structure.

A novel approach for optimal design of digital FIR filter using grasshopper optimization algorithm

The idea behind designing digital filters is to compute the optimal filter coefficients such that the magnitude response of the designed matches the ideal frequency response using optimization algorithms. The proposed work employed a recently proposed swarm-based optimization technique, namely, a grasshopper optimization algorithm (GOA) to design a linear phase finite impulse response (FIR) low pass, high pass, band pass , and band stop filters. This proposed algorithm models the behaviour of grasshoppers while seeking food sources to solve optimization problems. For the designing of the FIR filter, an absolute error difference fitness function is used, which is minimized using GOA to obtain optimal filter coefficients. The performance comparison of the proposed work is done with already existing algorithms such as cuckoo search, particle swarm optimization, artificial bee colony to prove its superiority and consistency. It is found that GOA based filter meets the objective efficiently with reduced ripples in pass band and higher attenuation in stop band with least execution time.

The Role of Electrocardiography in Occupational Medicine, from Einthoven's Invention to the Digital Era of Wearable Devices

Clinical-instrumental investigations, such as electrocardiography (ECG), represent a corollary of a procedures that, nowadays, is called upon as part of the principles of precision medicine. However when carrying out the professional routine examinations, most tend to ignore how a “simple” instrument can offer indispensable support in clinical practice, even in occupational medicine. The advent of the digital age, made of silicon and printed circuit boards, has allowed the miniaturization of the electronic components of these electro-medical devices. Finally, the adoption of patient wearables in medicine has been rapidly expanding worldwide for a number of years. This has been driven mainly by consumers’ demand to monitor their own health. With the ongoing research and development of new features capable of assessing and transmitting real-time biometric data, the impact of wearables on cardiovascular management has become inevitable. Despite the potential offered by this technology, as evident from the scientific literature, the application of these devices in the field of health and safety in the workplace is still limited. This may also be due to the lack of targeted scientific research. While offering great potential, it is very important to consider and evaluate ethical aspects related to the use of these smart devices, such as the management of the collected data relating to the physiological parameters and the location of the worker. This technology is to be considered as being aimed at monitoring the subject’s physiological parameters, and not at the diagnosis of any pathological condition, which should always be on charge of the medical specialist We conducted a review of the evolution of the role that electrophysiology plays as part of occupational health and safety management and on its possible future use, thanks to ongoing technological innovation.

Efficient QRS complex detection algorithm based on Fast Fourier Transform

An ECG signal, generally filled with noise, when de-noised, enables a physician to effectively determine and predict the condition and health of the heart. This paper aims to address the issue of denoising a noisy ECG signal using the Fast Fourier Transform based bandpass filter. Multi-stage adaptive peak detection is then applied to identify the R-peak in the QRS complex of the ECG signal. The result of test simulations using the MIT/BIH Arrhythmia database shows high sensitivity and positive predictivity (P_P) of 99.98 and 99.96% respectively, confirming the accuracy and reliability of proposed algorithm for detecting R-peaks in the ECG signal.

Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure

Heart rate monitoring and therapeutic devices include real-time sensing capabilities reflecting the state of the heart. Current circuitry can be interpreted as a cardiac electrical signal compression algorithm representing the time signal information into a single event description of the cardiac activity. It is observed that some detection techniques developed for ECG signal detection like artificial neural network, genetic algorithm, Hilbert transform, hidden Markov model are some sophisticated algorithms which provide suitable results but their implementation on a silicon chip is very complicated. Due to less complexity and high performance, wavelet transform based approaches are widely used. In this paper, after a thorough analysis of various wavelet transforms, it is found that Biorthogonal wavelet transform is best suited to detect ECG signal's QRS complex. The main steps involved in ECG detection process consist of de-noising and locating different ECG peaks using adaptive slope prediction thresholding. Furthermore, the significant challenges involved in the wireless transmission of ECG data are data conversion and power consumption. As medical regulatory boards demand a lossless compression technique, lossless compression technique with a high bit compression ratio is highly required. Furthermore, in this work, LZMA based ECG data compression technique is proposed. The proposed methodology achieves the highest signal to noise ratio, and lowest root mean square error. Also, the proposed ECG detection technique is capable of distinguishing accurately between healthy, myocardial infarction, congestive heart failure and coronary artery disease patients with a detection accuracy, sensitivity, specificity, and error of 99.92%, 99.94%, 99.92% and 0.0013, respectively. The use of LZMA data compression of ECG data achieves a high compression ratio of 18.84. The advantages and effectiveness of the proposed algorithm are verified by comparing with the existing methods.

Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems

Bradycardia can be modulated using the cardiac pacemaker, an implantable medical device which sets and balances the patient's cardiac health. The device has been widely used to detect and monitor the patient's heart rate. The data collected hence has the highest authenticity assurance and is convenient for further electric stimulation. In the pacemaker, ECG detector is one of the most important element. The device is available in its new digital form, which is more efficient and accurate in performance with the added advantage of economical power consumption platform. In this work, a joint algorithm based on biorthogonal wavelet transform and run-length encoding (RLE) is proposed for QRS complex detection of the ECG signal and compressing the detected ECG data. Biorthogonal wavelet transform of the input ECG signal is first calculated using a modified demand based filter bank architecture which consists of a series combination of three lowpass filters with a highpass filter. Lowpass and highpass filters are realized using a linear phase structure which reduces the hardware cost of the proposed design approximately by 50%. Then, the location of the R-peak is found by comparing the denoised ECG signal with the threshold value. The proposed R-peak detector achieves the highest sensitivity and positive predictivity of 99.75 and 99.98 respectively with the MIT-BIH arrhythmia database. Also, the proposed R-peak detector achieves a comparatively low data error rate (DER) of 0.002. The use of RLE for the compression of detected ECG data achieves a higher compression ratio (CR) of 17.1. To justify the effectiveness of the proposed algorithm, the results have been compared with the existing methods, like Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.