An efficient data compression and encryption technique for PPG signal

Dhar, S.; Mukhopadhyay, Sunil; Pal, Saurabh; Mitra, Madhuchhanda

doi:10.1016/j.measurement.2017.11.006

Cited by 32 publications

(9 citation statements)

References 44 publications

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“…where µ x denotes the mean of the original PPG signal [80]. This is also referred to as "percentage root mean squared difference (PRD)" [80], [82], [87]. The PRDN (PRD) is most widely used for assessing the quality of a decompressed signal or measuring distortion between the original and decompressed signal [80], [82], [87], [85].…”

Section: Existing Objective Distortion Measures For Assessing Decompressed Ppg Qualitymentioning

confidence: 99%

“…This is also referred to as "percentage root mean squared difference (PRD)" [80], [82], [87]. The PRDN (PRD) is most widely used for assessing the quality of a decompressed signal or measuring distortion between the original and decompressed signal [80], [82], [87], [85]. In [83], quality control mechanism was implemented by using two objective quality criteria: the PRD to check the global reconstruction quality and maximum absolute error to measure local distortion in the PPG signal.…”

Section: Existing Objective Distortion Measures For Assessing Decompressed Ppg Qualitymentioning

confidence: 99%

“…In [82], the normalized cross correlation (NCC) measure is used to determine the similarity between the original and reconstructed PPG signal. The NCC is computed as…”

Section: ) Normalized Cross Correlationmentioning

confidence: 99%

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Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

2022

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Real-time photoplethysmogram (PPG) denoising and data compression has become most essential requirements for accurately measuring vital parameters and efficient data transmission but that may introduce different kinds of waveform distortions due to the lossy processing techniques. Subjective quality assessment tests are the most reliable way to assess the quality, but they are time expensive and also cannot be incorporated with quality-driven compression mechanism. Thus, finding a best objective distortion measure is highly demanded for automatically evaluating quality of reconstructed PPG signal that must be subjectively meaningful and simple. In this paper, we present four types of objective distortion measures and evaluate their performance in terms of quality prediction accuracy, Pearson correlation coefficient and computational time. The performance evaluation is performed on different kinds of PPG waveform distortions introduced by the predictive coding, compressed sampling, discrete cosine transform and discrete wavelet transform. On the normal and abnormal PPG signals taken from five standard databases, evaluation results showed that different subjective quality evaluation groups (5-point, 3-point and 2-point rating scale) had different best objective distortion measures in terms of prediction accuracy and Pearson correlation coefficient. Moreover, selection of a best objective distortion measure depends upon type of PPG features that need to be preserved in the reconstructed signal.

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Section: Existing Objective Distortion Measures For Assessing Decompressed Ppg Qualitymentioning

confidence: 99%

Section: Existing Objective Distortion Measures For Assessing Decompressed Ppg Qualitymentioning

confidence: 99%

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Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

2022

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“…The PPG signal is widely used in clinical settings to monitor physiological parameters related to the cardiorespiratory system [ 21 ]. It is complex.…”

Section: Introductionmentioning

confidence: 99%

Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry

Pedro-Carracedo

Fuentes-Jimenez

Ugena

et al. 2021

Sensors

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This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal’s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the (p,q)-planes specific to the 0–1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed’s biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them.

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“…There is a tradeoff between compression of a signal (compression ratio) and minimizing information loss [ 14 ]. Data compression has been explored for biomedical signals previously, but the evaluation of these methods has been limited to one type of sensor, including either electrocardiogram (ECG) [ 14 , 15 , 16 , 17 ], photoplethysmography (PPG) [ 18 , 19 , 20 ], accelerometry [ 21 , 22 ], and electrodermal activity (EDA) [ 23 ]. Data compression methods can be divided into two groups: Lossless compression and lossy compression.…”

Section: Introductionmentioning

confidence: 99%

Biosignal Compression Toolbox for Digital Biomarker Discovery

Bent

Kim

et al. 2021

Sensors

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A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

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An efficient data compression and encryption technique for PPG signal

Cited by 32 publications

References 44 publications

Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry

Biosignal Compression Toolbox for Digital Biomarker Discovery

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