On-Device Integrated PPG Quality Assessment and Sensor Disconnection/Saturation Detection System for IoT Health Monitoring

Reddy, Gangireddy Narendra Kumar; Manikandan, M. Sabarimalai; Murty, N. V. L. Narasimha

doi:10.1109/tim.2020.2971132

Cited by 44 publications

(24 citation statements)

References 28 publications

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“…Although there are huge technological advancements in wearable or portable health monitoring devices, there many challenges that needs to be addressed: (i) Frequent false alarms due to the unavoidable motion artefacts, signal saturation and other noises in continuous health monitoring scenarios [14]- [19]; (ii) limited battery power leads to key challenge to the continuous sensing, processing and transmission of data wirelessly to the remote server [17]- [20]; (iii) frequent sensor's disconnection or movements under ambulatory or exercise conditions [14]; and (iv) malfunctioning of sensor and battery leads to the signal saturation or clipping [21]. By considering the significance of quality of PPG signal, there is need for exploring lightweight signal quality assessment (SQA) or signal quality indicator (SQI) for discarding the noisy PPG signals since the distorted PPG may produce noisy pulse measurements which can lead to inaccurate or unreliable diagnosis [3], [21].…”

Section: B Key Limitations Of Existing Ppg Processing Systemsmentioning

confidence: 99%

“…In many SQA methods, sets of fiducial and non-fiducial features were extracted from the original and filtered sensor signals, and differenced sensor signals by processing with different signal processing techniques such as digital filters, short-term Fourier transform (STFT), wavelet transform (WT), empirical mode decomposition (EMD), ensemble EMD (EEMD) and variational mode decomposition (VMD), independent component analysis (ICA) and adaptive filters [2], [14], [21]. However, computational complexity of the methods was not addressed with reference to resourceconstrained on-device processing in the past studies.…”

Section: A Proposed Time-domain Feature Based Ppg Signal Quality Asse...mentioning

confidence: 99%

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Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

et al. 2023

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Objective: Due to the high demands of tiny, compact, lightweight and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often corrupted severely under ambulatory and exercise recording conditions that leads to frequent false alarms. Method: In this paper, we propose a unified qualityaware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding technique for jointly performing signal quality assessment (SQA), data compression and pulse rate (PR) and respiration rate (RR) estimation without use of different domains of signal processing techniques that can be achieved by using the features extracted from the smoothed prediction error signal. Results: By using the five standard PPG databases, the performance of the proposed unified framework is evaluated in terms of compression ratio (CR), mean absolute error (MAE), false alarm reduction rate (FARR), processing time (PT) and energy saving (ES). The compression, PR and RR estimation and SQA results are compared with that of the existing methods and also with results of uncompressed PPG signals with sampling rates of 125 Hz and 25 Hz. Conclusion: The proposed unified quality-aware framework achieves an average CR of 4%, SQA (Se of 92.00%, FARR of 84.87%), PR (MAE: 0.46 ±1.20) and RR (MAE: 1.75 (0.65-4.45), PT (sec) of 15.34 ±0.01) and ES of 70.28% which outperforms the results of uncompressed PPG signal with a sampling rate of 125 Hz. Significance: Arduino Due computing platform based implementation demonstrates the real-time feasibility of the proposed unified quality-aware PR-RR estimation and data compression and transmission framework on the limited computational resources. Thus, it has great potential in improving energy-efficiency and trustworthiness of wearable and edge PPG monitoring devices.

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Section: B Key Limitations Of Existing Ppg Processing Systemsmentioning

confidence: 99%

Section: A Proposed Time-domain Feature Based Ppg Signal Quality Asse...mentioning

confidence: 99%

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

et al. 2023

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“…Therefore, most of the reported works are either validated using laboratory dataset, or annotated from other public datasets like MIMIC III. Out of the reported works summarised in table 1, only [5,6,10,13] and [19] showed SQA implementation in real hardware. Three very recent works [5,6] and [19] implemented SQA with Ac of 93.21%, 97.76% and 97.49% respectively, and memory requirement of 29 kB, 6.4 kB and 13.2 kB respectively.…”

Section: Hardware Implementationmentioning

confidence: 99%

“…With the popularization of ondevice measurements backed by powerful computing technology, PPG signal quality assessment (PSQA) assumes great importance [4]. For IoT-based measurement and arrhythmia monitoring, PSQA can reduce the battery life of the acquisition device and false alarms, respectively [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the works model the problem as a binary classification problem and detect the PPG corrupted segments/beats as an abnormal pattern. The notable approaches of PSQA include waveform morphology analysis [5,6,8], template-matching [9], Bayesian hypothesis testing [10], Gaussian filter [11], time and period domain features [12,13], higher order statistical features [14,15], timefrequency spectral features [16], pulse wave anomaly analysis from multisensory PPG [17], machine learning and deep learning [18], autocorrelation and pulse parameter extraction [19], and entropy-based features [20,21]. Table 1 shows a structured summary of selective recent works on PSQA published over last 10 years along with the present work in the last row.…”

Section: Introductionmentioning

confidence: 99%

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Real-time multi-class signal quality assessment of photoplethysmography using machine learning technique

Prasun¹,

Mukhopadhyay²,

Gupta³

2021

Meas. Sci. Technol.

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Photoplethysmography (PPG) signal quality assessment (SQA) ensures improved measurements of various surrogate cardiovascular measurements like heart rate, SpO2, blood pressure, cardiac output and many more and as well reduces false alrams in ambulatory measurements. Although PPG SQA (PSQA) is a well researched area, but multiclass prediction of signal quality and its hardware implementation is limited. In this paper, a new non-segmenting approach for multiclass PSQA is presented with an optimal set of seven time-frequency domain features in conjuction with supervised classifiers to identify clean, partly clean and corrupted PPG data. The present work was carried out in three major stages- signal (dataset) acquisition, feature extraction and 3-class classification using random forest classifier. The technique was evaluated with volunteers’ data, MIMIC-III data from PhysioNet, CSL data, and combined data and achieved an overall average accuracy of 96.8% with good (0.98 or above) sensitivity, specificity, F1 score, precision and recall. On-device implementation of the proposed PSQA was accomplished using 7.2 h of PPG dataset on a quad-core Broadcom BCM 2837 controller with 1.2 GHz, supported by 1 GB RAM-based standalone device, with an accuracy of 97.09%. The latency and the peak memory requirement of the implementation was 0.81 s and 19 kB respectively to process 3 s PPG data. The present technique may be integrated as a part of a remote healthcare system using wearable sensors.

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IoT for Enhanced Decision-Making in Medical Information Systems: A Systematic Review

Keikhosrokiani

2021

Studies in Fuzziness and Soft Computing

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On-Device Integrated PPG Quality Assessment and Sensor Disconnection/Saturation Detection System for IoT Health Monitoring

Cited by 44 publications

References 28 publications

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Real-time multi-class signal quality assessment of photoplethysmography using machine learning technique

IoT for Enhanced Decision-Making in Medical Information Systems: A Systematic Review

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