An Energy Efficient ECG Signal Processor Detecting Cardiovascular Diseases on Smartphone

Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.

Detection and classification of atrial and ventricular cardiovascular diseases to improve the cardiac health literacy for resource constrained regions

ECG is a non-invasive way of determining cardiac health by measuring the electrical activity of the heart. A novel detection technique for feature points P, QRS and T is investigated to diagnose various atrial and ventricular cardiovascular anomalies with ECG signals for ambulatory monitoring. Before the system is worthy of field trials, it is validated with several databases and recorded their response. The QRS complex detection is based on the Pan Tompkins algorithm and difference operation method that provides positive predictivity, sensitivity and false detection rate of 99.29%, 99.49% and 1.29%, respectively. Proposed novel T wave detection provides sensitivity of 97.78%. Also, proposed P wave detection provides positive predictivity, sensitivity and false detection rate of 99.43%, 99.4% and 1.15% for the control study (normal subjects) and 82.68%, 94.3% and 25.4% for the case (patients with cardiac anomalies) study, respectively. Disease detection such as arrhythmia is based on standard R-R intervals while myocardial infarction is based on the ST-T deviations where the positive predictivity, sensitivity and accuracy are observed to be 94.6%, 84.2% and 85%, respectively. It should be noted that, since the frontal leads are only used, the anterior myocardial infarction cases are detected with the injury pattern in lead avl and ST depression in reciprocal leads. Detection of atrial fibrillation is done for both short and long duration signals using statistical methods using interquartile range and standard deviations, giving very high accuracy, 100% in most cases. The system hardware for obtaining the 2 lead ECG signal is designed using commercially available off the shelf components. Small field validation of the designed system is performed at a Public Health Centre in Gujarat, India with 42 patients (both cases and controls). 78.5% accuracy was achieved during the field validation. It is thus concluded that the proposed method is ideal for improvisation in cardiac health monitoring outreach in resource constrained regions.

Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving

Continuous health monitoring in a vehicle enables the earlier detection of symptoms of cardiovascular diseases. In this work, we designed flexible and thin electrodes made of polyurethane for long-term electrocardiogram (ECG) monitoring while driving. We determined the time for reliable ECG recording to evaluate the effectiveness of the electrodes. We recorded data from 19 subjects under four scenarios: rest, city, highway, and rural. The recording time was five min for rest and 15 min for the other scenarios. The total recording (950 min) is publicly available under a CC BY-ND 4.0 license. We used the simultaneous truth and performance level estimation (STAPLE) algorithm to detect the position of R-waves. Then, we derived the RR intervals to compare the estimated heart rate with the ground truth, which we obtained from ECG electrodes on the chest. We calculated the signal-to-noise ratio (SNR) and averaged it for the different scenarios. Highway had the lowest SNR (-6.69 dB) and rural had the highest (-6.80 dB). The usable time of the steering wheel was 42.46% (city), 46.67% (highway), and 47.72% (rural). This indicates that steering-wheel-based ECG recording is feasible and delivers reliable recordings from about 45.62% of the driving time. In summary, the developed electrodes allow continuous in-vehicle heart rate monitoring, and our publicly available recordings provide the opportunity to apply more sophisticated data analytics.

Analysis of ECG Signals by Dynamic Mode Decomposition

OBJECTIVE

Based on cybernetics, a large system can be divided into subsystems, and the stability of each can determine the overall properties of the system. However, this stability analysis perspective has not yet been employed in electrocardiogram (ECG) signals. This is the first study to attempt to evaluate whether the stability of decomposed ECG subsystems can be analyzed in order to effectively investigate the overall performance of ECG signals, and aid in disease diagnosis.

METHODS

We used seven different cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block, dysrhythmia, hypertrophy, myocarditis, and valvular heart disease) to illustrate our method. Dynamic mode decomposition (DMD) was first used to decompose ECG signals into dynamic modes (DMs) which can be regarded as ECG subsystems. Then, the features related to the DMs stabilities were extracted, and nine common classifiers were implemented for classification of these pathologies.

RESULTS

Most features were significant for differentiating the above-mentioned groups (value<0.05 after Bonferroni correction). In addition, our method outperformed all existing methods for cardiac pathology classification.

CONCLUSION

We have provided a new spatial and temporal decomposition method, namely DMD, to study ECG signals.

SIGNIFICANCE

Our method can reveal new cardiac mechanisms, which can contribute to the comprehensive understanding of its underlying mechanisms and disease diagnosis, and thus, can be widely used for ECG signal analysis in the future.

A signal quality assessment-based ECG waveform delineation method used for wearable monitoring systems

Identifying transient and nonpersistent abnormal electrocardiogram (ECG) waveforms by continuously monitoring the high-risk populations is of great importance for the diagnosis, treatment, and prevention of cardiovascular diseases. In recent years, fabric electrodes have been widely used in wearable devices because of their non-irritating properties and better comfort than traditional AgCl electrodes. However, the motion noise caused by the relative movement between the fabric electrodes and skin affects the quality of ECGs and reduces the accuracy of diagnosis. Therefore, delineating the ECG waveforms that are recorded from wearable devices with varying levels of noise is still challenging. In this study, a signal quality assessment (SQA)-based ECG waveform delineation method that is used for wearable systems was developed. The ECG signal was first preprocessed by a bandpass filter. Five indices, including the multiscale nonlinear amplitude statistical distribution (adSQI₁, adSQI₂), the proportion of energy-related to T wave (ptSQI), and heart rates computed from R waves and T waves (rHR and tHR, respectively), were then calculated from the preprocessed ECG signal. The signals were classified as good, acceptable, and unacceptable ECGs by combining these indices through the use of a neural network. Subsequently, the R waves or/and T waves were identified for the corresponding feature interpretations based on the SQA results. ECGs that were recorded from the chest belts from 29 volunteers at different activity statuses were divided into 4-s segments. A total of 7133 manually labeled segments were used to derive (4985 segments) and validate (2148 segments) the algorithm. The adSQI₁, adSQI₂, tHR, and rHR characteristics were significantly different among the good, acceptable, and unacceptable ECGs. The ptSQI value was considerably higher in the good ECGs than in the acceptable and unacceptable ECGs. The ECG segments of different quality levels were classified with an accuracy of 96.74% by using the proposed SQA method. The R waves and T waves were identified with accuracies of 99.95% and 99.57%, respectively, for segments that were classified as acceptable and/or good. The SQA-based ECG waveform delineation method can perform reliable analysis and has the potential to be applied in wearable ECG systems for the early diagnosis and prevention of cardiovascular diseases.

Wearables for Industrial Work Safety: A Survey

Today, ensuring work safety is considered to be one of the top priorities for various industries. Workplace injuries, illnesses, and deaths often entail substantial production and financial losses, governmental checks, series of dismissals, and loss of reputation. Wearable devices are one of the technologies that flourished with the fourth industrial revolution or Industry 4.0, allowing employers to monitor and maintain safety at workplaces. The purpose of this article is to systematize knowledge in the field of industrial wearables’ safety to assess the relevance of their use in enterprises as the technology maintaining occupational safety, to correlate the benefits and costs of their implementation, and, by identifying research gaps, to outline promising directions for future work in this area. We categorize industrial wearable functions into four classes (monitoring, supporting, training, and tracking) and provide a classification of the metrics collected by wearables to better understand the potential role of wearable technology in preserving workplace safety. Furthermore, we discuss key communication technologies and localization techniques utilized in wearable-based work safety solutions. Finally, we analyze the main challenges that need to be addressed to further enable and support the use of wearable devices for industrial work safety.

A 1.2nW Analog Electrocardiogram Processor Achieving a 99.63% QRS Complex Detection Sensitivity

An energy-efficient electrocardiogram (ECG) processor for real-time QRS detection is presented. The proposed algorithm is based on the Pan-Tompkins algorithm and it is implemented in the analog domain leveraging ultra-low power analog electronics biased in subthreshold. Operational transconductance amplifiers with ∼100 mV linear range are used in almost all of the processing blocks, while squaring is performed on current signals. Additionally, instead of adaptive thresholding, a fixed-level thresholding is performed, thereby eliminating the need for additional blocks such as memory and threshold update. The processor is designed in 65 nm TSMC CMOS technology and has a footprint of 0.078 mm². When supplied by a 1 V supply, the processor consumes 1.2 nW. Using the recordings in the MIT-BIH database, the processor achieves an average QRS detection sensitivity of 99.63% and positive predictivity of 99.47%.

Novel Stable Capacitive Electrocardiogram Measurement System

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R-R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life.

A Low gate count reconfigurable architecture for biomedical signal processing applications

A new reconfigurable architecture for biomedical applications is presented in this paper. The architecture targets frequently encountered functions in biomedical signal processing algorithms thereby replacing multiple dedicated accelerators and reports low gate count. An optimized implementation is achieved by mapping methodologies to functions and limiting the required memory leading directly to an overall minimization of gate count. The proposed architecture has a simple configuration scheme with special provision for handling feedback. The effectiveness of the architecture is demonstrated on an FPGA to show implementation schemes for multiple DSP functions. The architecture has gate count of $$\approx$$ ≈ 25k and an operating frequency of 46.9 MHz.

A 13.34 μW Event-Driven Patient-Specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors

Artificial neural network (ANN) and its variants are favored algorithm in designing cardiac arrhythmia classifier (CAC) for its high accuracy. However, the implementation of ultralow power ANN-CAC is challenging due to the intensive computations. Moreover, the imbalanced MIT-BIH database limits the ANN-CAC performance. Several novel techniques are proposed to address the challenges in the low power implementation. Firstly, continuous-in-time discrete-in-amplitude (CTDA) signal flow is adopted to reduce the multiplication operations. Secondly, conditional grouping scheme (CGS) in combination with biased training (BT) is proposed to handle the imbalanced training samples for better training convergency and evaluation accuracy. Thirdly, arithmetic unit sharing with customized high-performance multiplier improves the power efficiency. Verified in FPGA and synthesized in 0.18 μm CMOS process, the proposed CTDA ANN-CAC can classify an arrhythmia within 252 μs at 25 MHz clock frequency with average power of 13.34 μW for 75bpm heart rate. Evaluated on MIT-BIH database, it shows over 98% classification accuracy, 97% sensitivity, and 94% positive predictivity.

ECG Classification Algorithm Based on STDP and R-STDP Neural Networks for Real-Time Monitoring on Ultra Low-Power Personal Wearable Devices

This paper presents a novel ECG classification algorithm for inclusion as part of real-time cardiac monitoring systems in ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption in real-time classification of ECG signals is significantly smaller. In specific, energy consumption is 1.78  μJ per beat, which is 2 to 9 orders of magnitude smaller than previous neural network based ECG classification methods.

A Battery-Less Portable ECG Monitoring System With Wired Audio Transmission

This paper proposes a batteryless electrocardiogram (ECG) monitoring chip and intelligent system with wired audio transmission, which can be applied to long-term and real-time ECG monitoring. The ECG signal is modulated and amplified in the front-end chip and then is transmitted through the microphone channel of the 3.5-mm headphone cable. The sinusoidal signals from the left and right channels are converted into a dc supply and a precise local oscillator signal in the front-end chip, respectively. The proposed system samples the signal through the high-precision audio analog-to-digital converter in smart devices and processes it through the internal software. Therefore, no external battery, local oscillator, and complicate modules are required in this system. A chopper/amplitude modulation (AM) reused mixer amplifier is proposed, which combines chopping with AM and reuses the local oscillator signal. The closed-loop gain of the proposed amplifier varies between 20 and 47 dB and can be automatically adjusted according to the amplitude of the output signal. The proposed chip was implemented in a standard 0.18-μm CMOS process and occupies 1.07 × 0.95 mm² of core area. The power management module outputs a 1.5-V dc voltage to power the system. When connected with the headphone cable load, the chip consumes a total power of 156 μW. The proposed monitoring system has been verified by the audio device and MATLAB in the PC. The test results show that the input reference noise of the demodulated signal is 2.12 μV_rms (0.1-200 Hz) and the total harmonic distortion is 0.56%@3 mV input.

Decision Tree Predictive Learner-Based Approach for False Alarm Detection in ICU

In this work, a novel method has been proposed for false alarm detection in Intensive Care Unit (ICU) during arrhythmia. To detect false alarm, various inputs are used such as electrocardiogram (ECG) signals, atrial blood pressure (ABP), photoplethysmogram signals (PLETH) and respiration (RESP). The inputs are given to decision tree predictive learner (DTPL) based classifier for thedetection of false alarm. The proposed method has an accuracy of 97% for prediction of false alarm in ICU. Theresult of the proposed method is promising which suggest that it can be used effectively for false alarm detection in ICUs. To the best of our knowledge, there is no such assumption based classification approach.

A Low Power Cardiovascular Healthcare System With Cross-Layer Optimization From Sensing Patch to Cloud Platform

Nowadays, cardiovascular disease is still one of the primary diseases that limit life expectation of humans. To address this challenge, this work reports an Internet of Medical Things (IoMT)-based cardiovascular healthcare system with cross-layer optimization from sensing patch to cloud platform. A wearable ECG patch with a custom System-on-Chip (SoC) features a miniaturized footprint, low power consumption, and embedded signal processing capability. The patch also integrates wireless connectivity with mobile devices and cloud platform for optimizing the complete system. On the big picture, a "wearable patch-mobile-cloud" hybrid computing framework is proposed with cross-layer optimization for performance-power trade-off in embedded-computing. The measurement results demonstrate that the on-patch compression ratio of the raw ECG signal can reach 12.07 yielding a percentage root mean square variation of 2.29%. In the test with the MIT-BIH database, the average improvement of signal to noise ratio and mean square error are 12.63 dB and 94.47%, respectively. The average accuracy of disease prediction operation executed in cloud platform is 97%.

Integrated IoT intelligent system for the automatic detection of cardiac variability

Detection of abnormal cardiac events during clinical examination is a matter of chances, as such events may not happen at that precise moment. We therefore propose the implementation and evaluation of a mobile based system that allows a real-time detection of cardiovascular problems related to heart-rate variability. Our approach is to integrate an Internet of Things eHealth kit based on Arduino and validated algorithms for heart rate variability to build a low-cost, reliable and scalable solution. 12 healthy users have evaluated the system in different scenarios to assess the best performing algorithm and the best windowing interval. Finally, a mobile system based on an Android application which integrated the Pan and Tompkins algorithm with a 20 seconds windowing and a module to retrieve real-time electrocardiography through a Bluetooth interface was implemented and assessed.

Design of high performance QRS complex detector for wearable healthcare devices using biorthogonal spline wavelet transform

A high performance QRS complex detector applicable for wearable healthcare devices is proposed in the present work. Since, higher SNR results in better detection accuracy and lesser number of coefficients reduces the hardware resources as well as power dissipation during on chip implementation. Biorthogonal spline wavelet transform is chosen for the proposed detector as it has high signal to noise ratio (SNR) and uses only four coefficients for decomposition. In the proposed approach, a Biorthogonal wavelet filter bank with fourth level decomposition is first used to separate the different frequency components and then a fourth level wavelet filter bank is used to get the denoised electrocardiogram (ECG) signals. Wavelet filter bank outputs are multiplied and soft threshold method is applied to get the QRS complex peaks by the QRS complex peak detector block. Add and shift multiplier used in the earlier designs has been replaced by a Booth multiplier in our approach to achieve the higher performance. Booth multiplier and QRS complex peak detector blocks have been designed for low hardware complexity, high performance and accurate detection of the QRS complex peaks. Time interval between the consecutive QRS peaks is calculated using the R-R peak time calculator block and the heart rate (HR) by the HR calculator block. Heart Rate Variability (HRV) and arrhythmia are detected based on these heart rate calculations. Proposed design has been tested for its robustness on multiple datasets (namely, MIT-BIH arrhythmia, MIT-BIH noise stress test, and MIT-BIH atrial fibrillation databases). Sensitivity of 99.31%, positive predictivity of 99.19% and the Detection Error Rate (DER) of 1.49% shown by the proposed design makes it preferable for QRS complex detectors used in wearable healthcare devices.

Noncontact Wearable Wireless ECG Systems for Long-Term Monitoring

Electrocardiography (ECG) is the most common and extensively used vital sign monitoring method in modern healthcare systems. Different designs of ambulatory ECG systems were developed as alternatives to the commonly used 12-lead clinical ECG systems. These designs primarily focus on portability and user convenience, while maintaining signal integrity and lowering power consumption. Here, a wireless ECG monitoring system is developed using flexible and dry capacitive electrodes for long-term monitoring of cardiovascular health. Our capacitive-coupled dry electrodes can measure ECG signals over a textile-based interface material between the skin and electrodes. The electrodes are connected to a data acquisition system that receives the raw ECG signals from the electrodes and transmits the data using Bluetooth to a computer. A software application was developed to process, store, and display the ECG signal in real time. ECG measurements were obtained over different types of textile materials and in the presence of body movements. Our experimental results show that the performance of our ECG system is comparable to other reported ECG monitoring systems. In addition, to put this research into perspective, recent ambulatory ECG monitoring systems, ECG systems-on-chip, commercial ECG monitoring systems, and different state-of-the-art ECG systems are reviewed, compared, and critically discussed.

Smartphone-based analytical biosensors

With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health.

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.