Abstract
This article focuses on ECG signal recognition based on acoustic feature extraction techniques. The SVM and k-NN classification approaches are proposed for recognizing the ECG heart sound as well as for calculating the recognition efficiency. In this proposed technique, ECG signals are previously transformed into a successive series of Mel-frequency cepstral coefficients for computing the acoustic features in terms of mean value. A histogram based understandable and new approach is proposed at this point for recognition of ‘P’ wave, ‘R’ wave etc. from ECG waveform. The recognition of ECG signal and their distinguishing features provide significant effort for the analysis. Here three statistical data with their detection efficiency estimation of histograms is analyzed from ECG signals from database. The entire method has been applied for convenience to different ECG record files taken from MIT-BIH database. Twelve leads are used from multi-lead ECG database which contains a 3600 Hz sampling frequency. The entire algorithm is executed on MATLAB R2014a. In this, the proposed method performance efficiency is evaluated.
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References
Bae TW, Kwon KK (2019) Efficient real-time R and QRS detection method using a pair of derivative filters and max filter for portable ECG device. ApplSci 9(19):4128. https://doi.org/10.3390/app9194128
Chen TE, Yang SI, Ho LT, Tsai KH, Chen YH, Chang YF, Lai YH, Wang SS, Tsao Y, Wu CC (2016) S1 and S2 heart sound recognition using deep neural networks. IEEE Trans Biomed Eng 64(2):372–380. https://doi.org/10.1109/tbme.2016.2559800
D’Aloia M, Longo A, Rizzi M (2019) Noisy ECG signal analysis for automatic peak detection. Information 10(2):35. https://doi.org/10.3390/info.10020035
Dokur Z, Ölmez T (2020) Heartbeat classification by using a convolutional neural network trained with Walsh functions. Neural ComputAppl. https://doi.org/10.1007/s00521-020-04709-w
Fira CM, Goras L (2008) An ECG signals compression method and its validation using NNs. IEEE Trans Biomed Eng 55(4):1319–1326. https://doi.org/10.1109/tbme.2008.918465
Halder B, Mitra S, Mitra M (2016) Detection and identification of ECG waves by histogram approach. In: 2016 2nd international conference on control, instrumentation, energy & communication (CIEC) 2016, IEEE, pp 168–172. https://doi.org/10.1109/ciec.2016.7513749
Jangra M, Dhull SK, Singh KK (2020) ECG arrhythmia classification using modified visual geometry group network (mVGGNet). J Intell Fuzzy Syst. https://doi.org/10.3233/jifs-191135
Laguna P, Jané R, Caminal P (1994) Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput Biomed Res 27(1):45–60. https://doi.org/10.1006/cbmr.1994.1006
Li H, Wei X, Zuo S, Dou Q, Ding M, Cao L, Gong Z, Wang R, Chen X, Wang B, Prades JD (2020) Arrhythmia classification algorithm based on multi-feature and multi-type optimized SVM. Am Sci Res J EngTechnolSci 63(1):72–86
Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M, Kobayashi M, Faust O, Acharya UR (2020) Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med 103: https://doi.org/10.1016/j.artmed.2019.101789
Lin WH, Ji N, Wang L, Li G (2019) A characteristic filtering method for pulse wave signal quality assessment. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC) 2019. IEEE, Berlin, pp 603–606. https://doi.org/10.1109/EMBC.2019.8856811.
Lu Z, Kim DY, Pearlman WA (2000) Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Trans Biomed Eng 47(7):849–856. https://doi.org/10.1109/10.846678
Martis RJ, Acharya UR, Mandana KM, Ray AK, Chakraborty C (2012) Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert SystAppl 39(14):11792–11800. https://doi.org/10.1016/J.cswa.2012.04.072
Sahu N, Peng D, Sharif H (2020) An innovative approach to integrate unequal protection-based steganography and progressive transmission of physiological data. SN ApplSci 2(2):237. https://doi.org/10.1007/s42452-020-1992-0
Shao M, Zhou Z, Bin G, Bai Y, Wu S (2020) A wearable electrocardiogram telemonitoring system for atrial fibrillation detection. Sensors 20(3):606. https://doi.org/10.3390/s20030606
Singh MK, Singh AK, Singh N (2018a) Acoustic comparison of electronics disguised voice using different semitones. Int J EngTechnol (UAE) 7(2):98. https://doi.org/10.14419/ijet.v7i2.16.11502
Singh MK, Singh AK, Singh N (2018b) Disguised voice with fast and slow speech and its acoustic analysis. Int J Pure Appl Math 118(14):241–246
Singh MK, Singh AK, Singh N (2019a) Multimedia analysis for disguised voice and classification efficiency. Multimed Tools Appl 78(20):29395–29411. https://doi.org/10.1007/s11042-018-6718-6
Singh M, Nandan D, Kumar S (2019b) Statistical analysis of lower and raised pitch voice signal and its efficiency calculation. Traitement du Signal 36(5):455–461. https://doi.org/10.18280/ts.360511
Singh MK, Singh N, Singh AK (2019c) Speaker's voice characteristics and similarity measurement using Euclidean distances. In: 2019 international conference on signal processing and communication (ICSC). IEEE, pp 317–322. https://doi.org/10.1109/icsc.45622.2019.8938366
Sivaparthipan CB, Karthikeyan N, Karthik S (2020) Designing statistical assessment healthcare information system for diabetics analysis using big data. Multimed Tools Appl 79:8431–8444. https://doi.org/10.1007/s11042-018-6648-3
Wang J, Wang P, Wang S (2019) Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process. Biomed Signal Process Control 55:101662. https://doi.org/10.1016/j.bspc.2019.101662
Yeh YC, Wang WJ, Chiou CW (2009) Cardiac arrhythmia diagnosis method using linear disciminant analysis on ECG signals. Measurement 42(5):778–89. https://doi.org/10.1016/j.measurement.2009.01.004
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Arpitha, Y., Madhumathi, G.L. & Balaji, N. Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique. J Ambient Intell Human Comput 13, 757–767 (2022). https://doi.org/10.1007/s12652-021-02926-2
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DOI: https://doi.org/10.1007/s12652-021-02926-2