A 0.8-μW and 74-dB High-Pass Sigma-Delta Modulator With OPAMP Sharing and Noise-Coupling Techniques for Biomedical Signal Acquisition

This work presents a third-order high-pass sigma-delta modulator (HPSDM) for biomedical signal acquisition. The operational amplifier (op-amp) sharing and noise-coupling techniques are adopted to reduce the required quantity of op-amps and add a noise-shaping order, which can achieve low power consumption and high resolution. A novel switched-capacitor architecture is proposed to suppress the increasing in-band noise and alleviate the circuit sensitivity to capacitor mismatch in the high-pass integrator. The proposed HPSDM was fabricated in a 0.18-μm standard CMOS process. Measurement results reveal that the proposed HPSDM has a signal-to-noise and distortion ratio (SNDR) of 75.26/74 dB in 200 Hz bandwidth and consumes 1.52/0.8 μW under 1.2/1 V supply voltage, which can achieve a peak Schreier Figure-of-Merit of 156.45/157.98 dB and a peak Walden FoM of 0.802/0.488 pJ/conv.

Low Complexity Binarized 2D-CNN Classifier for Wearable Edge AI Devices

Wearable Artificial Intelligence-of-Things (AIoT) devices exhibit the need to be resource and energy-efficient. In this paper, we introduced a quantized multilayer perceptron (qMLP) for converting ECG signals to binary image, which can be combined with binary convolutional neural network (bCNN) for classification. We deploy our model into a low-power and low-resource field programmable gate array (FPGA) fabric. The model requires 5.8× lesser multiply and accumulate (MAC) operations than known wearable CNN models. Our model also achieves a classification accuracy of 98.5%, sensitivity of 85.4%, specificity of 99.5%, precision of 93.3%, and F1-score of 89.2%, along with dynamic power dissipation of 34.9 μW.

An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices

Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric. Our model achieves a classification accuracy of 97.3%, sensitivity of 91.3%, specificity of 98.1%, precision of 86.7%, and F1-score of 88.9%, along with dynamic power dissipation of only 10.5-μW.

A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network

The heart is one of the human body’s vital organs. An electrocardiogram (ECG) provides continuous tracings of the electrophysiological activity originated from heart, thus being widely used for a variety of diagnostic purposes. This study aims to design and realize an artificial intelligence (AI)-based abnormal heart beat detection with applications for early detection and timely treatment for heart diseases. A convolutional neural network (CNN) was employed to achieve a fast and accurate identification. In order to meet the requirements of the modularity and scalability of the circuit, modular and efficient processing element (PE) units and activation function modules were designed. The proposed CNN was implemented using a TSMC 0.18 μm CMOS technology and had an operating frequency of 60 MHz with chip area of 1.42 mm2 and maximum power dissipation of 4.4 mW. Furthermore, six types of ECG signals drawn from the MIT-BIH arrhythmia database were used for performance evaluation. Results produced by the proposed hardware showed that the discrimination rate was 96.3% with high efficiency in calculation, suggesting that it may be suitable for wearable devices in healthcare.

High-Pass Sigma-Delta Modulator With Techniques of Operational Amplifier Sharing and Programmable Feedforward Coefficients for ECG Signal Acquisition

A high-pass sigma-delta modulator (HPSDM) is proposed for electrocardiography (ECG) signal acquisition system. The HPSDM is implemented using operational amplifier (op-amp) sharing and programmable feedforward coefficients. The op-amp sharing is adopted to reduce the quantity of amplifiers because they dominate the power consumption of the HPSDM. In addition, given that the magnitude of the ECG is dependent on different persons, programmable feedforward coefficients are utilized to extend the dynamic range of the HPSDM to fit the actual application. The proposed HPSDM is fabricated in a 0.18-μm standard CMOS process. Measurement results reveal that the proposed HPSDM has a signal-to-noise and distortion ratio (SNDR) of 54.5 dB and a power consumption of 2.25 μW under a 1.2 V supply voltage and achieves a figure of merit (FoM) of 12.96 pJ/conv. Moreover, the proposed HPSDM has an SNDR of 64.8 dB and a power consumption of 5.2 μW under a 1.8 V supply voltage and achieves a FoM of 9.15 pJ/conv due to the op-amp sharing technique. Under the 1.2 V and 1.8 V supply voltages, the dynamic range of the HPSDM is extended to approximately 12 dB due to the technique of programmable feedforward coefficients.

Wireless body area networks: a comprehensive survey

Current progress in technology, especially wireless body area network (WBAN) became one of the enabling technologies that provide many successful applications in non-medical or medical field. WBAN is a communication standard optimised for low power devices and operation on, in or around the human body, to monitor the human health issues and route the physical or vital data from biosensors nodes to the server for further analysis. The challenge of WBAN is that the energy of the biosensors is limited by the battery life and many routing protocols have been designed specifically for relaying collected data to a base station for additional processing, it might differ depending on the network architecture. This paper based on the recent publications provides a WBAN survey.

A Real-Time Vital-Sign Monitoring in the Physical Domain on a Mixed-Signal Reconfigurable Platform

This work presents a mixed-signal physical-compu-tation-electronics for monitoring three vital signs; namely heart rate, blood pressure, and blood oxygen saturation; from electrocardiography, arterial blood pressure, and photoplethysmography signals in real-time. The computational circuits are implemented on a reconfigurable and programmable signal-processing platform, namely field-programmable analog array (FPAA). The design leverages the core enabling technology of FPAA, namely floating-gate CMOS devices, and an on-chip low-power microcontroller to achieve energy-efficiency while not compromising accuracy. The custom physical-computation-electronics operating in CMOS subthreshold region, performs low-level (i.e., physiologically-relevant feature extraction) and high-level (i.e., detecting arrhythmia) signal processing in an energy-efficient manner. The on-chip microcontroller is used (1) in the programming mode for controlling the charge storage at the analog-memory elements to introduce patient-dependency into the system and (2) in the run mode to quantify the vital signs. The system has been validated against digital computation results from MATLAB using datasets collected from three healthy subjects and datasets from the MIT/BIH open source database. Based on all recordings in the MIT/BIH database, ECG R-peak detection sensitivity is 94.2%. The processor detects arrhythmia in three MIT/BIH recordings with an average sensitivity of 96.2%. The cardiac processor achieves an average percentage mean error bounded by 3.75%, 6.27%, and 7.3% for R-R duration, systolic blood pressure, and oxygen saturation level calculations; respectively. The power consumption of the ECG, blood-pressure and photo-plethysmography processing circuitry are 126 nW, 251 nW and 1.44 μW respectively in a 350 nm process. Overall, the cardiac processor consumes 1.82 μW.

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.

Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer

Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies.

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.

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%.

Segmentation of the ECG Signal by Means of a Linear Regression Algorithm

The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.

Towards an Architecture to Guarantee Both Data Privacy and Utility in the First Phases of Digital Clinical Trials

In the era of the Internet of Things (IoT), drug developers can potentially access a wealth of real-world, participant-generated data that enable better insights and streamlined clinical trial processes. Protection of confidential data is of primary interest when it comes to health data, as medical condition influences daily, professional, and social life. Current approaches in digital trials entail that private user data are provisioned to the trial investigator that is considered a trusted party. The aim of this paper is to present the technical requirements and the research challenges to secure the flow and control of personal data and to protect the interests of all the involved parties during the first phases of a clinical trial, namely the characterization of the potential patients and their possible recruitment. The proposed architecture will let the individuals keep their data private during these phases while providing a useful sketch of their data to the investigator. Proof-of-concept implementations are evaluated in terms of performances achieved in real-world environments.

A Nanowatt Real-Time Cardiac Autonomic Neuropathy Detector

This paper presents an electrocardiogram (ECG) processor on chip for full ECG feature extraction and cardiac autonomic neuropathy (CAN) classification. Full ECG extraction is performed using absolute value curve length transform (A-CLT) for $\text{QRS}_{\text{peak}}$ detection and using low-pass differentiation for other ECG features such as $\text{QRS}_{\text{on}}$, $\text{QRS}_{\text{off}}$, Pwave, and Twave. The proposed QRS detector attained a sensitivity of 99.37% and predictivity of 99.38%. The extracted $\text{QRS}_{\text{peak}}$ to $\text{QRS}_{\text{peak}}$ intervals (RR intervals) along with QT intervals enable CAN severity detection, which is a cardiac arrhythmia usually seen in diabetic patients leading to increased risk of sudden cardiac death. This paper presents the first hardware real-time implementation of CAN severity detector that is based on RR variability and QT variability analysis. RR variability metrics are based on mean RR interval and root mean square of standard differences of the RR intervals. The proposed architecture was implemented in 65-nm technology and consumed 75 nW only at 0.6 V, when operating at 250 Hz. Ultralow power dissipation of the system enables it to be integrated into wearable healthcare devices.

Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.

From Pacemaker to Wearable: Techniques for ECG Detection Systems

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical research scenario places notable importance on techniques and methods to detect CVDs. As adduced by world health organization, technological proceeds in the field of cardiac function assessment have become the nucleus and heart of all leading research studies in CVDs in which electrocardiogram (ECG) analysis is the most functional and convenient tool used to test the range of heart-related irregularities. Most of the approaches present in the literature of ECG signal analysis consider noise removal, rhythm-based analysis, and heartbeat detection to improve the performance of a cardiac pacemaker. Advancements achieved in the field of ECG segments detection and beat classification have a limited evaluation and still require clinical approvals. In this paper, approaches on techniques to implement on-chip ECG detector for a cardiac pacemaker system are discussed. Moreover, different challenges regarding the ECG signal morphology analysis deriving from medical literature is extensively reviewed. It is found that robustness to noise, wavelet parameter choice, numerical efficiency, and detection performance are essential performance indicators required by a state-of-the-art ECG detector. Furthermore, many algorithms described in the existing literature are not verified using ECG data from the standard databases. Some ECG detection algorithms show very high detection performance with the total number of detected QRS complexes. However, the high detection performance of the algorithm is verified using only a few datasets. Finally, gaps in current advancements and testing are identified, and the primary challenge remains to be implementing bullseye test for morphology analysis evaluation.

Two-Wire Bus Combining Full Duplex Body-Sensor Network and Multilead Biopotential Measurements

Classical approaches to make high-quality measurements of biopotential signals require the use of shielded or multiwire cables connecting the electrodes to a central unit in a star arrangement. As a consequence, increasing the number of leads increases cabling and connector complexity, which is not only limiting the patient comfort but is also anticipated as the main limiting factor to future miniaturization and cost reduction of tomorrow's wearables. We have recently introduced a novel sensing architecture that significantly reduces the cabling complexity by eliminating shielded or multiwire cables and by allowing simple connectors, thanks to a bus arrangement. In this architecture, electrodes are replaced by so-called cooperative sensors that require synchronous operation for systems larger than two sensors. This paper presents a novel full duplex body-sensor network based on a simple two-wire bus that combines biopotential measurements, synchronization, and gathering of data in a single cooperative sensor with a throughput up to 2 Mb/s. When compared to others, the suggested approach is advantageous as it keeps the cabling complexity at its minimum and does not require every sensor to be equipped with wireless communication capabilities. First experimental measurements demonstrated the reliability of the approach for a wearable 12-lead electrocardiogram monitoring system tested in real-life scenario.

Evaluation of an Automated Swallow-Detection Algorithm Using Visual Biofeedback in Healthy Adults and Head and Neck Cancer Survivors

Mobile health (mHealth) technologies may offer an opportunity to address longstanding clinical challenges, such as access and adherence to swallowing therapy. Mobili-T^® is an mHealth device that uses surface electromyography (sEMG) to provide biofeedback on submental muscles activity during exercise. An automated swallow-detection algorithm was developed for Mobili-T^®. This study evaluated the performance of the swallow-detection algorithm. Ten healthy participants and 10 head and neck cancer (HNC) patients were fitted with the device. Signal was acquired during regular, effortful, and Mendelsohn maneuver saliva swallows, as well as lip presses, tongue, and head movements. Signals of interest were tagged during data acquisition and used to evaluate algorithm performance. Sensitivity and positive predictive values (PPV) were calculated for each participant. Saliva swallows were compared between HNC and controls in the four sEMG-based parameters used in the algorithm: duration, peak amplitude ratio, median frequency, and 15th percentile of the power spectrum density. In healthy participants, sensitivity and PPV were 92.3 and 83.9%, respectively. In HNC patients, sensitivity was 92.7% and PPV was 72.2%. In saliva swallows, HNC patients had longer event durations (U = 1925.5, p < 0.001), lower median frequency (U = 2674.0, p < 0.001), and lower 15th percentile of the power spectrum density [t(176.9) = 2.07, p < 0.001] than healthy participants. The automated swallow-detection algorithm performed well with healthy participants and retained a high sensitivity, but had lowered PPV with HNC patients. With respect to Mobili-T^®, the algorithm will next be evaluated using the mHealth system.

Matrix-Inversion-Free Compressed Sensing With Variable Orthogonal Multi-Matching Pursuit Based on Prior Information for ECG Signals

Low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN) are presented. The prior probability of ECG sparsity in the wavelet domain is first exploited. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm that consists of two phases is proposed. In the first phase, orthogonal matching pursuit (OMP) algorithm is adopted to effectively augment the support set with reliable indices and in the second phase, the orthogonal multi-matching pursuit (OMMP) is employed to rescue the missing indices. The reconstruction performance is thus enhanced with the prior information and the vOMMP algorithm. Furthermore, the computation-intensive pseudo-inverse operation is simplified by the matrix-inversion-free (MIF) technique based on QR decomposition. The vOMMP-MIF CS decoder is then implemented in 90 nm CMOS technology. The QR decomposition is accomplished by two systolic arrays working in parallel. The implementation supports three settings for obtaining 40, 44, and 48 coefficients in the sparse vector. From the measurement result, the power consumption is 11.7 mW at 0.9 V and 12 MHz. Compared to prior chip implementations, our design shows good hardware efficiency and is suitable for low-energy applications.

Polymer-Based Organic Batteries

The storage of electric energy is of ever growing importance for our modern, technology-based society, and novel battery systems are in the focus of research. The substitution of conventional metals as redox-active material by organic materials offers a promising alternative for the next generation of rechargeable batteries since these organic batteries are excelling in charging speed and cycling stability. This review provides a comprehensive overview of these systems and discusses the numerous classes of organic, polymer-based active materials as well as auxiliary components of the battery, like additives or electrolytes. Moreover, a definition of important cell characteristics and an introduction to selected characterization techniques is provided, completed by the discussion of potential socio-economic impacts.

Flexible quality of service model for wireless body area sensor networks

Wireless body area sensor networks (WBASNs) are becoming an increasingly significant breakthrough technology for smart healthcare systems, enabling improved clinical decision-making in daily medical care. Recently, radio frequency ultra-wideband technology has developed substantially for physiological signal monitoring due to its advantages such as low-power consumption, high transmission data rate, and miniature antenna size. Applications of future ubiquitous healthcare systems offer the prospect of collecting human vital signs, early detection of abnormal medical conditions, real-time healthcare data transmission and remote telemedicine support. However, due to the technical constraints of sensor batteries, the supply of power is a major bottleneck for healthcare system design. Moreover, medium access control (MAC) needs to support reliable transmission links that allow sensors to transmit data safely and stably. In this Letter, the authors provide a flexible quality of service model for ad hoc networks that can support fast data transmission, adaptive schedule MAC control, and energy efficient ubiquitous WBASN networks. Results show that the proposed multi-hop communication ad hoc network model can balance information packet collisions and power consumption. Additionally, wireless communications link in WBASNs can effectively overcome multi-user interference and offer high transmission data rates for healthcare systems.

Robust cardiac event change detection method for long-term healthcare monitoring applications

A long-term continuous cardiac health monitoring system highly demands more battery power for real-time transmission of electrocardiogram (ECG) signals and increases bandwidth, treatment costs and traffic load of the diagnostic server. In this Letter, the authors present an automated low-complexity robust cardiac event change detection (CECD) method that can continuously detect specific changes in PQRST morphological patterns and heart rhythms and then enable transmission/storing of the recorded ECG signals. The proposed CECD method consists of four stages: ECG signal quality assessment, R-peak detection and beat waveform extraction, temporal and RR interval feature extraction and cardiac event change decision. The proposed method is tested and validated using both normal and abnormal ECG signals including different types of arrhythmia beats, heart rates and signal quality. Results show that the method achieves an average sensitivity of 99.76%, positive predictivity of 94.58% and overall accuracy of 94.32% in determining the changes in heartbeat waveforms of the ECG signals.