Comparison of the Signal Processing Methods to Enhance the Performance of the Signal Reconstruction System with Deep Learning

Jang, Young In; Kwon, Nam Kyu

doi:10.23919/ascc56756.2022.9828320

Cited by 4 publications

(8 citation statements)

References 11 publications

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“…We include papers regarding a fine-grained reconstruction of both a physiological signal or simplified waveform derived from a physiological signal. We find papers about the reconstruction of the electrocardiogram (ECG) (n = 3) [32], [36], [63], the seismocardiogram (SCG) [44] (n = 1) and the photoplethismogram (PPG) (n = 2) [69], [72]; then we find papers dealing with respiratory waveform (n = 4) [68], [71], [90], [94] or simplified version of the cardiac waveform (n = 4) [35], [41], [60], [73] or both (n = 2) [30], [77].…”

Section: A First Cluster Tasksmentioning

confidence: 99%

“…[23], [30], [32], [35], [36], [41], [44], [60], [63], [68]- [73], [77], [90], [94] Physiological event detection (n = 7)…”

Section: A First Cluster Tasksmentioning

confidence: 99%

“…From Table V, we can notice that the authors made an effort to make the dataset balanced between the two sex. The synchronisation between the RADAR recording and the reference is solved by different methods, such as Precision Time Protocol [30], [68], [94], custom LABVIEW program [73], [100], post-processing manual synchronisation based on clearly defined time events [107], binary shared sequences [78], [108] or by matching of the time stamps [17], [40], [65], [66], [72], [87]. Moreover, we find two methods used to deal with synchronisation errors.…”

Section: B Datasets General Informationmentioning

confidence: 99%

“…The last row of reference processing refers to the elaboration of the reference signal needed to reduce the amount of information used in the training process. Some of the typical processing aim at simplifying the reference signal by either extracting specific cardiac events (n = 4), such as the R-peak [28], [78], [87] or Aortic Opening [81], or by removing some of the frequency components (n = 2) [72], [95] or by transforming the complex nature of the ECG signal in simple periodical shapes (n = 4) [22], [35], [41], [73].…”

Section: ) Performance Metricsmentioning

confidence: 99%

“…[53] Range Azimuth [58], [65], [99] Range Doppler [17], [29] Working with I/Q data [68], [69], [87], [94] Phase extraction (e.g. arctan) [22], [25], [28]- [30], [32], [34]- [36], [40], [41], [44], [55], [57], [59]- [61], [64]- [66], [70], [72]- [76], [78], [79], [81], [82], [85], [86], [89], [95]-[103] Unwrap [25], [52], [60], [64], [97] Envelope extraction [51], [78], [79] Normalisation or standardisation [23], [28], [51]- [53], [73] Filtering Bandpass or highpass filter w...…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

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Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

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In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

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