Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review
Abstract
:1. Introduction
2. Background
2.1. Cough Physiology and Acoustic Properties
2.2. Detection vs. Classification
3. Method
4. Results
4.1. Referenced Studies
4.1.1. Cough Classification Studies
4.1.2. Cough Detection Studies
Motivation | References | Number |
---|---|---|
Objective monitoring | [17,22,28,74,75,76,78,80,88,133,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163] | 34 |
Remote/self/lab-free monitoring | [119,120,121,122,123,124,125,126,127,128,129,130,131,134,135,138,164,165,166,167,168,169,170,171,172] | 25 |
Disease assessment | [14,27,69,73,132,137,139,173,174,175,176,177,178,179,180,181,182] | 17 |
Disease diagnosis | [49,54,83,84,85,86,87,183,184,185] | 10 |
Methodology | [68,77,136,186,187] | 5 |
4.1.3. Other Studies
4.1.4. Publication Years
4.2. Data and Subjects
4.2.1. Sensors and Acquisition Systems
4.2.2. Subjects and Protocol
4.3. Methods
4.3.1. Approach
4.3.2. Features
4.3.3. Classifiers
4.4. Validation
4.4.1. Cough Classification
4.4.2. Cough Detection
4.5. Citations
4.6. Most Complete Detection Studies
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref. | Cough Type | Subject Characteristics | Lung Condition | Disease | ||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Wet | Dry | Productive | Non-Productive | Spontaneous | Voluntary | Induced | Healthy | Unhealthy | Male | Female | Obstructive | Restrictive | Normal | Croup | Pneumonia | Asthma | Pertussis | COPD | Heart Failure | Tuberculosis | Bronchitis | Bronchiolitis | COVID-19 | Cold | LRTD | Upper RespiratoryTract Disease | n/a | |
[70,79,92,93,94,95,96,97] | X | X | ||||||||||||||||||||||||||
[98] | X | X | ||||||||||||||||||||||||||
[87] | X | X | ||||||||||||||||||||||||||
[99] | X | X | ||||||||||||||||||||||||||
[88] | X | X | ||||||||||||||||||||||||||
[33,100] | X | X | ||||||||||||||||||||||||||
[100] | X | X | ||||||||||||||||||||||||||
[29] | X | X | X | |||||||||||||||||||||||||
[30,101,102,103,104,105,106] | X | X | ||||||||||||||||||||||||||
[59,85,107] | X | |||||||||||||||||||||||||||
[59,84,85,89] | X | |||||||||||||||||||||||||||
[85,90,91,108] | X | |||||||||||||||||||||||||||
[105,109] | X | X | ||||||||||||||||||||||||||
[110] | X | X | ||||||||||||||||||||||||||
[83,111] | X | |||||||||||||||||||||||||||
[112] | X | X | X | |||||||||||||||||||||||||
[81] | X | X | ||||||||||||||||||||||||||
[113,114,115] | X | X | ||||||||||||||||||||||||||
[80] | X | |||||||||||||||||||||||||||
[58] | X | X | ||||||||||||||||||||||||||
[116] | X | X | X | |||||||||||||||||||||||||
[100] | X | X | ||||||||||||||||||||||||||
[86] | X | X | X | |||||||||||||||||||||||||
[105] | X | X | ||||||||||||||||||||||||||
[59] | X | |||||||||||||||||||||||||||
[85] | X | |||||||||||||||||||||||||||
[105,109] | X | X | ||||||||||||||||||||||||||
[85] | X | |||||||||||||||||||||||||||
[105] | X | X | ||||||||||||||||||||||||||
[85] | X | |||||||||||||||||||||||||||
[45,46,47,48,49,50,51,52,53,81] | X | X | ||||||||||||||||||||||||||
[48,52,54,55] | X | |||||||||||||||||||||||||||
[52] | X | X | ||||||||||||||||||||||||||
[54] | X | X | X | X | ||||||||||||||||||||||||
[56] | X | X | X | X | ||||||||||||||||||||||||
[81] | X | X | X | |||||||||||||||||||||||||
[78] | X | X | ||||||||||||||||||||||||||
[117,118] | X |
Group and Description | Examples |
---|---|
A—Cepstral coefficients: Coefficients obtained from cepstral analysis. | MFCC [145], improved MFCC [68], Gammatone Frequency Cepstral Coefficients [158] |
B—Timedomain distribution and regularity: Characterization of the time domain distribution. | ZCR [83], Shannon Entropy [150], Variance [100], Skewness [33], Kurtosis [94], Crest Factor [83] |
C—Total energy and power: Overall energy or power values, for which the explosive phase is supposed to present a sudden rise. | Total Energy [33], Log-Energy [68], Total Power [156], Average Power [33], Loudness [27] |
D—Pitch, prosody, formants and harmonics: Speech-related characteristics, supposed to detect voicing activity. | F0 [174], Pitch Standard Deviation [75], Pitch Coverage [75], Formant Frequencies [94] |
E—Spectral distribution and regularity: Characterization of the spectrum distribution. | Spectral Centroid [29], Spectral Bandwidth [121], Spectral Flatness [112], Skewness [70] |
F—Frequencies: Peculiar frequencies of the spectrum. | Spectral Rolloff [192], Dominant Frequency [83] |
G—Energy and power in specific bands: Energy or power values in specific frequency bands. | Power [30], Loudness [27], Log Spectral Energies [58], Octave Analysis [33] |
H—Energy and power ratios: Ratios of energy or power between different frequency bands. | Power Ratio [79], Relative Energy [132], Relative Power [121] |
I—Time, duration and rates: Duration and time dynamics values. | Duration [116], Slope [140], L-ratio [33], Left to Right Ratio [192], Rising Envelope Gradient [160] |
J—Frequency domain general: General spectrum characterization. | Power Spectral Density [99], Spectral Distances [99] |
K—Spectrogram images, moments and filterbanks: Overall spectrogram images, image moments and outputs of filterbanks. | Local Hu Moments [124], Cochleagram moments [84], Gabor Filterbank [98] |
L—Deep learning raw data: Input data for deep neural networks. | Mel-Spectrogram [54], Mel-Scaled Filter Banks [137] |
M—Time domain envelope: Characterization of the time domain envelope shape, capturing typically characteristic peaks in the explosive phase. | Filtered Envelope [69], Peak Number [79], Peak Location [160], Rate of Decay [100] |
N—Frequency domain ratios: Ratios between different characteristic of the spectrum. | Harmonic to Noise Ratio [132], High-Frequency Content [147], Low quantile ratio [147] |
O—Spectral variations: Time variation measures of the spectrum. | Spectral Variation [132], Spectral Flux [132], Evo [146] |
P—Time domain amplitude: Characterization of the time domain amplitude. | Maximum Value [113], Minimum Value [113], Amplitude [160] |
Q—Other | Wavelet [91], Katz Fractal Dimension [184], DeoxyriboNucleic Acid [81] |
Study and System | # Test Subjects (Cough Events) | Feature Groups (as in Table 3) + Classifiers | Results | Description |
---|---|---|---|---|
[121] | 13 (1309) | A − E − F − H − J − N + SVM | SE = 88–90, SP = 81–75 | Robust smartphone-based cough detection |
[22] HACC | 10 (237) | A + Probabilistic Neural Network | SE = 80, SP = 96 | Cough detection over long periods of time for objective monitoring |
[14] HACC-LCM | 18 | A + GMM-HMM | SE = 57.9, SP = 98.2, PREC = 80.9 | Objective cough monitoring for COPD patients |
[175] LCM | 9 (2151/1338) | A + GMM-HMM | SE = 71–82, FPh = 13–7 | Continuous cough detection for ambulatory patients |
[176] LCM | 26/9 | A + GMM-HMM | SE = 85.7–90.9, SP = 99.9–99.5, PREC = 94.7, FPh = 0.8–2.5 | Continuous cough detection over long periods of time for ambulatory patients |
[177] LCM | 23/9 | A + GMM-HMM | SE = 86–91, SP = 99, FPh = 1–2.5 | Continuous cough detection over long periods of time for ambulatory patients |
[150] | 10/14 (656/1434) | A − B − D + TDNN | SE = 89.8–92.8, SP = 94.8-97.5, ACC = 93.9-97.4 | Cough detection for pediatric population |
[28] LifeShirt | 8 (3645) | SE = 78.1, SP = 99.6, ACC = 99, PREC = 84.6 | Cough detection over long periods of time for ambulatory COPD patients | |
[160] | 10 (1019) | I − M − P + Decision Tree | SE = 90.2, SP = 96.5, ACC = 93.1, PREC = 96.7, F1 = 93.3 | Cough characterization and detection |
[17] KarmelSonix | 12 | SE = 96–90, SP = 94, PREC = 90–93, FPh = 1.2–1.2 | Objective cough monitoring for realistic ambulatory situations | |
[181] | 10 (50) | SE = 84, SP = 50, ACC = 67, PREC = 62.7, F1 = 71.8 | Separation of cough and throat clearing sounds | |
[78] | 15 (5489) | A − B − L + CNN | SE = 99.9, SP = 91.5, ACC = 99.8 | Overnight smartphone-based cough monitoring for asthma patients |
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Serrurier, A.; Neuschaefer-Rube, C.; Röhrig, R. Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review. Sensors 2022, 22, 2896. https://doi.org/10.3390/s22082896
Serrurier A, Neuschaefer-Rube C, Röhrig R. Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review. Sensors. 2022; 22(8):2896. https://doi.org/10.3390/s22082896
Chicago/Turabian StyleSerrurier, Antoine, Christiane Neuschaefer-Rube, and Rainer Röhrig. 2022. "Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review" Sensors 22, no. 8: 2896. https://doi.org/10.3390/s22082896
APA StyleSerrurier, A., Neuschaefer-Rube, C., & Röhrig, R. (2022). Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review. Sensors, 22(8), 2896. https://doi.org/10.3390/s22082896