Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

doi:10.3390/diagnostics8010010

Cited by 34 publications

(19 citation statements)

References 64 publications

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“…In this study, the feature points were extracted beat by beat, and the heart-beat pair was divided by the R wave of the ECG, which was identified by a reliable detector [ 22 , 23 , 24 ]. In one beat period, some feature points of PPG and its derivatives were defined [ 25 ], and the detailed waveforms and names are clearly marked in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database

et al. 2018

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Cardiovascular diseases (CVDs) have become the biggest threat to human health, and they are accelerated by hypertension. The best way to avoid the many complications of CVDs is to manage and prevent hypertension at an early stage. However, there are no symptoms at all for most types of hypertension, especially for prehypertension. The awareness and control rates of hypertension are extremely low. In this study, a novel hypertension management method based on arterial wave propagation theory and photoplethysmography (PPG) morphological theory was researched to explore the physiological changes in different blood pressure (BP) levels. Pulse Arrival Time (PAT) and photoplethysmogram (PPG) features were extracted from electrocardiogram (ECG) and PPG signals to represent the arterial wave propagation theory and PPG morphological theory, respectively. Three feature sets, one containing PAT only, one containing PPG features only, and one containing both PAT and PPG features, were used to classify the different BP categories, defined as normotension, prehypertension, and hypertension. PPG features were shown to classify BP categories more accurately than PAT. Furthermore, PAT and PPG combined features improved the BP classification performance. The F1 scores to classify normotension versus prehypertension reached 84.34%, the scores for normotension versus hypertension reached 94.84%, and the scores for normotension plus prehypertension versus hypertension reached 88.49%. This indicates that the simultaneous collection of ECG and PPG signals could detect hypertension.

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Section: Methodsmentioning

confidence: 99%

Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database

et al. 2018

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“…Among the latter, we point out the fast QRS detection algorithm proposed by Elgendi [35], as well as the general two event-related moving averages (TERMA) framework [36] by the same author, for being highly efficient, yet much simpler and faster than conventional QRS detectors. It is furthermore worth noting that Elgendi and colleagues in [37] and more recently in [38] were able to show the good performance characteristics of TERMA-based QRS detectors to also hold for compressed ECG signals. In particular, in [38], they were able to corroborate this for ECG records decimated by a factor of up to 8, making TERMA-based QRS detectors particularly well suited for mobile health applications in low-and middle-income countries (see also [39]).…”

Section: Introductionmentioning

confidence: 93%

“…It is furthermore worth noting that Elgendi and colleagues in [37] and more recently in [38] were able to show the good performance characteristics of TERMA-based QRS detectors to also hold for compressed ECG signals. In particular, in [38], they were able to corroborate this for ECG records decimated by a factor of up to 8, making TERMA-based QRS detectors particularly well suited for mobile health applications in low-and middle-income countries (see also [39]).…”

Section: Introductionmentioning

confidence: 93%

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

Lang

2018

Technologies

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While the importance of continuous monitoring of electrocardiographic (ECG) or photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, there remain numerous challenges to its widespread adoption. Most notably, difficulties arise regarding crucial characteristics such as real-time capability, computational complexity, the amount of required training data, and the avoidance of too-restrictive modeling assumptions. We propose a lightweight and model-free approach for the online detection of cardiac anomalies such as ectopic beats in ECG or PPG signals on the basis of the change detection capabilities of singular spectrum analysis (SSA) and nonparametric rank-based cumulative sum (CUSUM) control charts. The procedure is able to quickly detect anomalies without requiring the identification of fiducial points such as R-peaks, and it is computationally significantly less demanding than previously proposed SSA-based approaches. Therefore, the proposed procedure is equally well suited for standalone use and as an add-on to complement existing (e.g., heart rate (HR) estimation) procedures.

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“…For example, Mamaghanian et al [10] compress ECG data before transmission, extending mote lifetime by 37.1%. Elgendi et al [18] achieve a compression ratio of 6 for ECG data, while retaining 99.56% reconstruction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Culman

Aminikhanghahi

Cook

2020

Sensors

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Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.

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Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

Cited by 34 publications

References 64 publications

Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database

Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

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