A Wi-Fi IoT prototype for ECG monitoring exploiting a novel Compressed Sensing method

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 38
Author(s):  
Eulalia Balestrieri ◽  
Pasquale Daponte ◽  
Luca De Vito ◽  
Francesco Picariello ◽  
Sergio Rapuano ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Compressed Sensing ◽  
Battery Life ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Ecg Signals ◽  
Reconstruction Quality ◽  
Acquisition Device ◽  
Iot Devices

<p><span lang="EN-US">The paper presents an Internet of Things (IoT) prototype which consists of a data acquisition device wirelessly connected to Internet via Wi-Fi, for continuous electrocardiogram (ECG) monitoring. The proposed system performs a novel Compressed Sensing (CS) based method on ECG signal with the aim of reducing the amount of transmitted data, thus realizing an efficient way to increase the battery life of such devices. For the assessment of the energy consumption of the device, an experimental setup was arranged and its description is presented. The evaluation of the reconstruction quality of the ECG signal in terms of Percentage of Root-mean-squared Difference (PRD</span><span lang="EN-US">) is reported for several Compression Ratios (CRs</span><span lang="EN-US">). The obtained experimental results clearly demonstrate the robustness and usefulness of the Wi-Fi based IoT devices adopting the considered CS-method for data compression of ECG signals. Furthermore, it allows reducing the energy consumption of the IoT device, by increasing the CR</span><span lang="EN-US">, without significantly degrading the quality of the reconstructed ECG signal.</span></p>

scholarly journals The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

2021 ◽  
Vol 11 (13) ◽  
pp. 5908
Author(s):  
Raquel Cervigón ◽  
Brian McGinley ◽  
Darren Craven ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Atrial Fibrillation ◽  
Characteristic Curve ◽  
Set Partitioning ◽  
Constrained Systems ◽  
Battery Life ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Signal Compression ◽  
Ecg Signals ◽  
Cardiac Devices

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

scholarly journals A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Kan Luo ◽  
Zhipeng Cai ◽  
Keqin Du ◽  
Fumin Zou ◽  
Xiangyu Zhang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Compressed Sensing ◽  
Bayesian Learning ◽  
Total Energy Consumption ◽  
Binary Matrix ◽  
Ultra Low Power ◽  
Single Spot ◽  
Ecg Signals ◽  
Analog Front End ◽  
Communication Module

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node’s specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.

scholarly journals ECG signal classification based on Deep Learning by using Convolutional Neural Network (CNN)

2020 ◽  
Vol 3 (3) ◽  
pp. 12-23
Author(s):  
Aqeel M. Hamad alhussainy ◽  
Ammar D. Jasim
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
World Health ◽  
Geometric Transformation ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Result Show ◽  
Automatic Methods ◽  
Health Organization

Cardiovascular diseases (CVDs) are consider  the main cause  of death today According to World Health Organization (WHO),and because that ECG signal is very important tool in monitoring and diagnosis of these disease , different automatic methods were proposed based on this signal. [1]. The manual analysis of ECG signals is suffered different challenges such as differeculty of detecting and classify waveform of this signal, So, many machine learning methods  are  explored to describe  the anomalies ECG signal accurately . Deep learning (DL) can be used in ECG classification, it can improve the quality of the automatic classification system. In this paper , we have proposed a deep learning classification system by using  different layers of convolution, rectifier and pooling operations  that can be used to increase feature extraction of ECG signal.        We have proposed two models, one is used for input signal of 1-D, in which we designed model for classification csv type of data for ECG signal, while in the second proposed system, we used model for 2-D signal after convert it from its csv type .  2-D signal (ECG image) is used in order to augment the two dimensional signal with different methods to increase the accuracy of the model by training it with geometric transformation of the original input images such as rotation, shearing etc.The results are compared with AlexNet and other  models  based on the metrics, which are    used to measure the performance of the proposed work, the result show that, the proposed models improve the efficiency of the classification  in the two systems.

scholarly journals TWO-LEAD JOINT DETECTION ALGORITHM OF QRS COMPLEXES IN ECG SIGNAL

2021 ◽  
Author(s):  
WANSONG XU ◽  
TIANWU CHEN ◽  
FANYU DU
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Joint Detection ◽  
Ecg Signals ◽  
Detection Algorithms ◽  
Threshold Adjustment ◽  
Current Algorithm ◽  
Electrocardiogram Ecg

Objective: The detection of QRS complexes is an important part of computer-aided analysis of electrocardiogram (ECG). However, most of the existing detection algorithms are mainly for single-lead ECG signals, which requires high quality of signal. If the signal quality decreases suddenly due to some interference, then the current algorithm is easy to cause misjudgment or missed detection. To improve the detection ability of QRS complexes under sudden interference, we study the QRS complexes information on multiple leads in-depth, and propose a two-lead joint detection algorithm of QRS complexes. Methods: Firstly, the suspected QRS complexes are screened on the main lead. For the suspected QRS complexes with low confidence and the complexes that may be missed, further accurate detection and joint judgment shall be carried out at the corresponding position of the auxiliary lead. At the same time, the adaptive threshold adjustment algorithm and backtracking mechanism are used to modify the detection results. Results: The proposed detection algorithm is validated using 48 ECG records of the MIT-BIH arrhythmia database, and achieves average detection accuracy of 99.71%, sensitivity of 99.88% and positive predictivity of 99.81%. Conclusion: The proposed algorithm has high accuracy, which can effectively deal with the sudden interference of ECG signal. Meanwhile, the algorithm requires small amount of computation, and can be embedded into hardware for real-time detection.

scholarly journals Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

2020 ◽  
Author(s):  
Wentao Li ◽  
Mingxiong Zhao ◽  
Yuhui Wu ◽  
Junjie Yu ◽  
Lingyan Bao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Policy Design ◽  
Descent Method ◽  
Total Energy Consumption ◽  
Aerial Vehicle ◽  
Iot Devices ◽  
Mutually Independent

Abstract Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($\mathbf{P_T}$), resource allocation at UAV ($\mathbf{P_R}$) and offloading decisions at IoT devices ($\mathbf{P_O}$), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

Green IoT and NBIoT

2021 ◽  
pp. 273-300
Author(s):  
Pallavi Mishra
Keyword(s):  
Modern Society ◽  
Future Research ◽  
Battery Life ◽  
Energy Demands ◽  
Data Rates ◽  
Future Research Directions ◽  
Iot Devices ◽  
Real Time Information ◽  
Time Information

This chapter illustrates that there are many challenging problems in the modern society such as environment pollution, radiation pollution, high demand, and low supply of energy. Such issues need modern solutions to tackle them. In this context, green internet of things (IoT) solutions have come up with flying colors. As there is a constant need of the energy by the interconnected IoT devices to perceive, fetch, and transmit the real-time information, the energy demands remain high. Green IoT is an emerging concept to meet this problem by framing the energy-efficient policies so as to provide a simpler yet better solution to enhance the quality of the current practices. In this chapter, different practical aspects of green IoT and narrowband IoT (NBIoT) deployment have been presented. NBIoT narrowband signals are used in low data rates are transmitted and have a widerange of reception because narrow filters cancel out unwanted wideband noise. NBIoT has several advantages over LTE-M due to lower device cost, longer battery life and extended coverage. Finally, some future research directions have been addressed.

Quality Assessment of ECG Signal Based on Wavelet Energy Ratio and Wavelet Energy Entropy

2014 ◽  
Vol 530-531 ◽  
pp. 577-580 ◽  
Author(s):  
Ai Hua Zhang ◽  
Ming Chun Kou ◽  
Chen Diao ◽  
Dong Mei Lin
Keyword(s):  
Quality Assessment ◽  
Wavelet Decomposition ◽  
Energy Ratio ◽  
Ecg Signal ◽  
Quality Indices ◽  
Ecg Signals ◽  
Wavelet Energy ◽  
Energy Entropy ◽  
Wavelet Energy Entropy

ECG signal is affected by many factors such as noise and interference in the process of acquisition, which make it difficult for clinicians to interpret the ECG signal precisely and effectively. In order to detect whether an ECG signal is worthy to be interpreted by clinicians, an algorithm was proposed to assess the quality of ECG signal based on wavelet energy ratio and wavelet energy entropy. After wavelet decomposition, the ECG signals wavelet energy ratio and wavelet energy entropy were calculated in three different frequency bands, and we defined them as the quality indices to evaluate the quality of ECG signal. Experimental results show that we can achieve an accuracyof 95.2%.

Development of medical capacitive coupling electrodes using the skin-electrode contact control

Sensor Review ◽  
2020 ◽  
Vol 40 (3) ◽  
pp. 347-354
Author(s):  
Gennadiy Evtushenko ◽  
Inna A. Lezhnina ◽  
Artem I. Morenetz ◽  
Boris N. Pavlenko ◽  
Arman A. Boyakhchyan ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
The Body ◽  
Capacitive Coupling ◽  
Ecg Signal ◽  
Significant Deterioration ◽  
Content Type ◽  
Ecg Signals ◽  
Skin Electrode ◽  
Electrode Contact

Purpose The purpose of this paper is the development and study of capacitive coupling electrodes with the ability to monitor the quality of the skin–electrode contact in the process of electrocardiogram (ECG) diagnostics. The study’s scope embraces experimental identification of distortions contributed into the recorded ECG signal at various degrees of disturbance of the skin–electrode contact. Design/methodology/approach A capacitive coupling electrode is designed and manufactured. A large number of experiments was carried out to record ECG signals with different quality of the skin–electrode contact. Using spectral analysis, the characteristic distortions of the ECG signals in the event of contact disturbance are revealed. Findings It was found that the violation of the skin–electrode contact leads to significant deterioration in the recorded signal. In this case, the most severe distortions appear with various violations of the skin–electrode contact of two sensors in one lead. It has been experimentally shown that the developed sensor allows monitoring the quality of the contact, and therefore, improvement of the quality of signal registration, enabled by the use of bespoke processing algorithms. Practical implications These sensors will be used in personalized medicine devices and tele-ECG devices. Originality/value In this work, authors studied the effect of the skin–electrode contact of a capacitive electrode with the body on the quality of the recorded ECG signal. Based on the studies, the necessity of monitoring contact was shown to improve the quality of diagnostics provided by personalized medicine devices; the capacitive sensor with contact feedback was developed.

scholarly journals Collaborative offloading for UAV-enabled time-sensitive MEC networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wen-Tao Li ◽  
Mingxiong Zhao ◽  
Yu-Hui Wu ◽  
Jun-Jie Yu ◽  
Ling-Yan Bao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Trajectory Optimization ◽  
Policy Design ◽  
Descent Method ◽  
Total Energy Consumption ◽  
Aerial Vehicle ◽  
Iot Devices ◽  
Mutually Independent

AbstractRecently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

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