Acoustic Characteristics of Voice in Patients with Chronic Kidney Disease

El-gaber, Fatma Mohammed Abd; Sallam, Yossra; Sayed, Hanaa Mohammed Eid El

doi:10.2147/ijgm.s307684

Cited by 2 publications

(1 citation statement)

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“…이처럼 만성콩팥병으로 인한 호흡계와 후두 근육의 손상이 음성의 변화를 야기함에 따라 만성콩팥병 환자의 음성의 특징 에 대한 연구들이 다수 존재한다. 선행연구들에서는 /아/ 모음 의 연장 발화에서 음질[jitter, shimmer, harmonics-to-noise ratio( HNR)], 음높이(F0), 공기역학적(최대연장발성시간) 특징을 추 출하여 만성콩팥병 환자의 음성의 특성을 분석하고 대조군과 비교하였다 (Abd El-gaber et al, 2021;Hassan, 2014;Kumar & Bhat, 2010;Mudawwar et al, 2017;Zaky et al, 2020).…”

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Automatic detection and severity prediction of chronic kidney disease using machine learning classifiers*

Mun¹,

Kim²,

Kim³

et al. 2022

Phonetics Speech Sci.

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This paper proposes an optimal methodology for automatically diagnosing and predicting the severity of the chronic kidney disease (CKD) using patients' utterances. In patients with CKD, the voice changes due to the weakening of respiratory and laryngeal muscles and vocal fold edema. Previous studies have phonetically analyzed the voices of patients with CKD, but no studies have been conducted to classify the voices of patients. In this paper, the utterances of patients with CKD were classified using the variety of utterance types (sustained vowel, sentence, general sentence), the feature sets [handcrafted features, extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), CNN extracted features], and the classifiers (SVM, XGBoost). Total of 1,523 utterances which are 3 hours, 26 minutes, and 25 seconds long, are used. F1-score of 0.93 for automatically diagnosing a disease, 0.89 for a 3-classes problem, and 0.84 for a 5-classes problem were achieved. The highest performance was obtained when the combination of general sentence utterances, handcrafted feature set, and XGBoost was used. The result suggests that a general sentence utterance that can reflect all speakers' speech characteristics and an appropriate feature set extracted from there are adequate for the automatic classification of CKD patients' utterances.

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