PL EN
Classification of Cardiovascular disease using dysphonia measurement in speech
Abdelhamid Bourouhou 1  
,   Abdelilah Jilbab 1  
,   Chafik Nacir 1  
,   Ahmed Hammouch 1  
 
Więcej
Ukryj
1
Universite Mohammed V, Ecole Normale Superieure de l'Enseignement Technique de Rabat
AUTOR DO KORESPONDENCJI
Abdelhamid Bourouhou   

Universite Mohammed V, Ecole Normale Superieure de l'Enseignement Technique de Rabat
Data nadesłania: 27-09-2020
Data ostatniej rewizji: 18-12-2020
Data akceptacji: 19-01-2021
Data publikacji online: 25-01-2021
Data publikacji: 04-03-2021
 
Diagnostyka 2021;22(1):31–38
 
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
Cardiovascular disease is the leading cause of death worldwide. The diagnosis is made by non-invasive methods, but far from being comfortable, rapid, and accessible to everyone. Speech analysis is an emerging non-invasive diagnostic tool, and a lot of research has shown that it is efficient in speech recognition, and in detecting Parkinson's disease, so can it be effective for discrimination between patients with cardiovascular disease and healthy people? This present work answers the question posed, by collecting a database of 75 people, 35 of them suffering from cardiovascular diseases, and 40 are healthy. We took from each one, three vocal recordings of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii… ..). By measuring dysphonia in speech, we were able to extract 26 features, with which we will train three types of classifiers, the k-near-neighbor, the support vectors machine classifier, and the naive Bayes classifier. The methods were tested for accuracy and stability, and we obtained 81% accuracy as the best result using the k-near-neighbor classifier.
 
REFERENCJE (17)
3.
Rawther NN, Cheriyan J. Detection and classification of cardiac arrhythmias based on ECG and PCG using temporal and wavelet features. IJARCCE. 2015; 4(4).
 
4.
Bouguila Z, Moukadem A, Dieterlen A, Ahmed Benyahia A, Hajjam A, Talha S, Andres E. Autonomous cardiac diagnostic based on synchronized ECG and PCG signal. In: 7th International Joint Conference on Biomedical Engineering Systems and Technologies—ESEO, Angers. 2014.
 
5.
Ghassemian H, Kenari AR. Early detection of pediatric heart disease by automated spectral analysis of phonocardiogram in children. J. Inf. Syst. Telecommun. 2015; 3(2): 66–75.
 
6.
Nabih-Ali M, El-Dahshan E-SA, Yahia AS. Heart diseases diagnosis using intelligent algorithm based on PCGsignal analysis. Circuits Syst. 2017; 8(7):184–190.
 
7.
Levanon Y, Lossos-Shifrin L. Inventors; Google, assignee. Method and system for diagnosing pathological phenomenon using a voice signal 2008. US patent 7,398,213 B1.
 
8.
Bonneh YS, Levanon Y, Dean-Pardo O, Lossos L, Adini Y. Abnormal speech spectrum and increased pitch variability in young autistic children. Front Hum Neurosci. 2011;4:237.
 
9.
Uma Rani K, Holi MS. Automatic detection of neurological disordered voices using mel cepstral coefficients and neural networks. In: 2013 IEEE Point-of-Care Healthcare Technologies (PHT) 2013:76-79. Bangalore, India, 2013.
 
10.
Titze Ingo. Phonation into a straw as a voice building exercise. Journal of Singing. 2000; 57: 27-28.
 
11.
Cnockaert L, Schoentgen J, Auzou P, Ozsancak C, Defebvre L, Grenez F. Low-frequency vocal modulations in vowels produced by Parkinsonian subjects, Speech Communication. 2008;50(4):288-300. https://doi.org/10.1016/j.spec....
 
12.
Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig, LO. Suitability of dysphonia measurements for telemonitoring of Parkinson's Disease. IEEE Transactions on Biomedical Engineering. 2009;56(4):1015-1022. https://doi.org/10.1109/TBME.2....
 
13.
Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Trans Biomed Eng. 2012;59(5):1264-1271. https://doi.org/10.1109/TBME.2....
 
15.
Bourouhou A, Jilbab A, Nacir C, Hammouch A. Detection and localization algorithm of the S1 and S2 heart sounds. 2017 International Conference on Electrical and Information Technologies (ICEIT), Rabat. 2017:1-4 https://doi.org/10.1109/EITech....
 
16.
Bourouhou A, Jilbab A, Nacir C, Hammouch A. Comparison of classification methods to detect the Parkinson disease. 2016 International Conference on Electrical and Information Technologies (ICEIT), Tangiers, 2016:421-424. https://doi.org/10.1109/EITech....
 
17.
Bourouhou A, Jilbab A, Nacir C, Hammouch A. Heart Sounds classification for a medical diagnostic assistance. International Journal of Online and Biomedical Engineering (iJOE) 2019; 15(11): 88–103.
 
eISSN:2449-5220
ISSN:1641-6414