Abstract
We adopt Bidirectional Long Short-Term Memory (BiLSTM) neural network and Wavelet Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting speech impairments of patients at the early stage of central nervous system disorders (CNSD). The study includes 339 voice samples collected from 15 subjects: 7 patients with early stage CNSD (3 Huntington, 1 Parkinson, 1 cerebral palsy, 1 post stroke, 1 early dementia), other 8 subjects were healthy. Speech data is collected using voice recorder from Neural Impairment Test Suite (NITS) mobile app. Features are extracted from pitch contours, Mel-frequency cepstral coefficients (MFCC), Gammatone cepstral coefficients (GTCC), Gabor (analytic Morlet) wavelet and auditory spectrograms. 94.50% (BiLSTM) and 96.3% (WST-SVM) accuracy is achieved for solving healthy vs. impaired classification problem. The developed method can be applied for automated CNSD patient health state monitoring and clinical decision support systems as well as a part of Internet of Medical Things (IoMT).
Highlights
Central nervous system disorders (CNSD) include Huntington Disease (HD), Parkinson Disease (PD), Alzheimer Disease (AD), mild cognitive impairment (MCI) and dementia
HD and PD have many different symptoms, which are related only to that one specific disease, they present a similar set of deficits expressed in speech e.g. slow, weak, imprecise, The associate editor coordinating the review of this manuscript and approving it for publication was Wei Wei
The classifiers used were extreme learning machine (ELM), K-nearest neighbour (KNN), probabilistic neural network (PNN) and
Summary
Central nervous system disorders (CNSD) include Huntington Disease (HD), Parkinson Disease (PD), Alzheimer Disease (AD), mild cognitive impairment (MCI) and dementia These diseases cover a broad range of symptoms, in particular, tremor (muscle stagnancy, body balance disorders, involuntary movements, etc.), cognitive (decision-making difficulties, behavioral disorders, attention problems, memory loss, etc.), speech (lack of pronounced words, use of shorter phrases, pauses) and energy expenditure (weight loss, negative energy balance) impairments [1]. Uncoordinated speech (dysarthria) [4], swallowing difficulties (dysphagia) [5], trouble sequencing the sounds in syllables and words (apraxia) [6], difficulty to express thoughts orally (aphasia) [7] Such circumstances ( combined with cognitive impairments) lead to the need of specialized assessment and speech treatment for people with HD or PD. SLP gives guidelines for maintaining safe swallowing, evaluates speech acceptance criteria i.e. pitch (degree of voice highness or lowness), loudness (ability for patient to project his own voice), articulation (ability to pronounce sounds), voice quality (ability to hold pitch properly), respiration (coordination of speech with breathing), resonance (quality of voice that is determined by the balance of sound vibration during speech), prosody (rhythm, stress and intonation during speaking) [5]–[7]
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