Automated Detection of Cardinal Points of ECG Signal for Feature Extraction Using a Single Median Filter

Paul, Avishek; Das, Nantu; Pal, Saurabh; Mitra, Madhuchhanda

doi:10.1007/s40031-022-00754-2

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…1 when used in ECG signals (Tables 1 and 2 ). They are located in the first quartile, so that only 4 of them have a higher accuracy [ 23 , 30 , 32 , 45 ]. However, none of these reflect their effectiveness on data derived from the PPG signal, so we cannot establish a comparison with our proposal.…”

Section: Discussionmentioning

confidence: 99%

“… [ 20 ] MBA BPF; HT; AT 99.50 [ 45 ] MBA; QT; CPSC20195 NN 99.96 [ 21 ] MBA BPF; Variance; AT 99.69 [ 22 ] MBA BPF; Exponencial Transform; AT 99.71 [ 34 ] QT; NST; INCART; CPSC2019 a WT; NN 97.48 ECG split into 10 s. [ 43 ] MBA EV; AT 99.64 Integrated detection of P and T waves. [ 46 ] MBA; ESTT; INCART; MSA NN 98.09 [ 23 ] MBA BPF; MF 99.96 Integrated detection of P and T waves. [ 24 ] MBA BPF; SE; HT 99.69 [ 25 ] MBA; QT BPF; normalized cubic power; AT 99.84 Integrated detection of P and T waves.…”

Section: Introductionmentioning

confidence: 99%

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Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes

et al. 2023

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The analysis of cardiac activity is one of the most common elements for evaluating the state of a subject, either to control possible health risks, sports performance, stress levels, etc. This activity can be recorded using different techniques, with electrocardiogram and photoplethysmogram being the most common. Both techniques make significantly different waveforms, however the first derivative of the photoplethysmographic data produces a signal structurally similar to the electrocardiogram, so any technique focusing on detecting QRS complexes, and thus heartbeats in electrocardiogram, is potentially applicable to photoplethysmogram. In this paper, we develop a technique based on the wavelet transform and envelopes to detect heartbeats in both electrocardiogram and photoplethysmogram. The wavelet transform is used to enhance QRS complexes with respect to other signal elements, while the envelopes are used as an adaptive threshold to determine their temporal location. We compared our approach with three other techniques using electrocardiogram signals from the Physionet database and photoplethysmographic signals from the DEAP database. Our proposal showed better performances when compared to others. When the electrocardiographic signal was considered, the method had an accuracy greater than 99.94%, a true positive rate of 99.96%, and positive prediction value of 99.76%. When photoplethysmographic signals were investigated, an accuracy greater than 99.27%, a true positive rate of 99.98% and positive prediction value of 99.50% were obtained. These results indicate that our proposal can be adapted better to the recording technology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes

et al. 2023

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“…These artifacts are eliminated using a median lter, which typically behaves like a low pass lter. Detail operation regarding the median lter and the respective denoising performance is elaborated in [21].…”

Section: Pre-processing Of the Ppg Signalmentioning

confidence: 99%

Mental stress detection using multimodal characterization of PPG signal for personal healthcare applications

Paul¹,

Chakraborty

Sadhukhan

et al. 2023

Preprint

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With growing complexities in our society, mental stress has become inevitable in every human life. Long-term mental stress condition could instigate several chronic diseases and thus require its early evaluation. Existing mental stress estimation techniques mostly uses complicated, multi-channel and expert dependent electroencephalogram (EEG) based approaches. Moreover, the respiratory signal presents promising stress-related information, but its acquisition is also complicated and needs multimodal assistance. Hence, in this research a unique approach based on multimodal characterization of the easy-to-acquire Photoplethysmogram (PPG) signal is proposed to assess the stressed condition. Exclusively, the developed algorithm not only uses a primary PPG feature, but also derives the respiratory rate from the same PPG signal via simplified methodologies. The technique is evaluated on the PPG recordings collected from the publicly available DEAP dataset. Efficiency of these easy-to-compute features is then assessed via a simple threshold-based classification technique to categorize the stressed and the relaxed conditions with an average accuracy of 98.43%. Compared to the existing methods, the proposed algorithm not only shows improved performance but the associated simple methodology with minimum acquisition load also justifies its applicability in real-time standalone, personal healthcare applications.

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Automated Classification of Happy and Sad Emotional States from PPG Signal Using Time Domain Analysis

Paul,

Das,

Pal

et al. 2024

Studies in Autonomic, Data-Driven and Industrial Computing

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Automated Detection of Cardinal Points of ECG Signal for Feature Extraction Using a Single Median Filter

Cited by 7 publications

References 28 publications

Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes

Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes

Mental stress detection using multimodal characterization of PPG signal for personal healthcare applications

Automated Classification of Happy and Sad Emotional States from PPG Signal Using Time Domain Analysis

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