Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS

肌萎缩侧索硬化 延髓麻痹 听力学 吞咽 医学 多元分析 构音障碍 支持向量机 随机森林 语音识别 疾病 计算机科学 人工智能 内科学 外科
作者
Alberto Tena,F. Clariá,Francesc Solsona,Mónica Povedano
出处
期刊:Computer Methods and Programs in Biomedicine [Elsevier BV]
卷期号:229: 107309-107309 被引量:5
标识
DOI:10.1016/j.cmpb.2022.107309
摘要

Bulbar dysfunction is a term used in amyotrophic lateral sclerosis (ALS). It refers to motor neuron disability in the corticobulbar area of the brainstem which leads to a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar dysfunction is voice deterioration characterized by grossly defective articulation, extremely slow laborious speech, marked hypernasality and severe harshness. Recently, research efforts have focused on voice analysis to capture this dysfunction. The main aim of this paper is to provide a new methodology to diagnose this dysfunction automatically at early stages of the disease, earlier than clinicians can do.The study focused on the creation of a voiceprint consisting of a pattern generated from the quasi-periodic components of a steady portion of the five Spanish vowels and the computation of the five principal and independent components of this pattern. Then, a set of statistically significant features was obtained using multivariate analysis of variance and the outcomes of the most common supervised classification models were obtained.The best model (random forest) obtained an accuracy, sensitivity and specificity of 88.3%, 85.0% and 95.0% respectively when classifying bulbar vs. control participants but the results worsened when classifying bulbar vs. no-bulbar patients (accuracy, sensitivity and specificity of 78.7%, 80.0% and 77.5% respectively for support vector machines). Due to the great uncertainty found in the annotated corpus of the ALS patients without bulbar involvement, we used a safe semi-supervised support vector machine to relabel the ALS participants diagnosed without bulbar involvement as bulbar and no-bulbar. The performance of the results obtained increased, especially when classifying bulbar and no-bulbar patients obtaining an accuracy, sensitivity and specificity of 91.0%, 83.3% and 100.0% respectively for support vector machines. This demonstrates that our model can improve the diagnosis of bulbar dysfunction compared not only with clinicians, but also the methods published to date.The results obtained demonstrate the efficiency and applicability of the methodology presented in this paper. It may lead to the development of a cheap and easy-to-use tool to identify this dysfunction in early stages of the disease and monitor progress.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
充电宝应助氮化碳采纳,获得10
刚刚
1秒前
长安完成签到,获得积分10
1秒前
尽我所能发布了新的文献求助10
1秒前
最後まで发布了新的文献求助10
2秒前
ll应助chen采纳,获得10
3秒前
1111应助我最好命采纳,获得20
3秒前
林林发布了新的文献求助40
3秒前
3秒前
七仔发布了新的文献求助10
4秒前
anny2022完成签到,获得积分10
4秒前
4秒前
Rosaline发布了新的文献求助10
4秒前
5秒前
5秒前
小蓬牖完成签到,获得积分10
6秒前
鲤鱼平安发布了新的文献求助10
6秒前
6秒前
dadadasds完成签到,获得积分10
7秒前
7秒前
demom完成签到 ,获得积分10
8秒前
在水一方应助hhhyyy采纳,获得10
8秒前
星辰大海应助细腻海蓝采纳,获得10
8秒前
8秒前
慕慕倾完成签到,获得积分10
9秒前
科目三应助fyl采纳,获得10
9秒前
xbc完成签到,获得积分10
9秒前
tianhaizhi发布了新的文献求助10
10秒前
泥花完成签到,获得积分10
11秒前
huichuanyin完成签到 ,获得积分10
11秒前
充电宝应助守护星星采纳,获得10
11秒前
124完成签到,获得积分10
11秒前
HJ皎完成签到,获得积分10
11秒前
汉堡包应助小蓬牖采纳,获得10
12秒前
oneday完成签到,获得积分10
12秒前
今后应助谨慎山彤采纳,获得10
12秒前
Hello应助科研通管家采纳,获得10
13秒前
yydragen应助科研通管家采纳,获得50
13秒前
ll应助科研通管家采纳,获得10
13秒前
13秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969222
求助须知:如何正确求助?哪些是违规求助? 3514124
关于积分的说明 11171948
捐赠科研通 3249361
什么是DOI,文献DOI怎么找? 1794799
邀请新用户注册赠送积分活动 875431
科研通“疑难数据库(出版商)”最低求助积分说明 804779