Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions

Motin, Mohammod Abdul; Pah, Nemuel Daniel; Raghav, Sanjay; Kumar, Deepak

doi:10.1109/access.2022.3203973

Cited by 7 publications

(10 citation statements)

References 48 publications

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“…Vowel: 59 [ 17 , 40 , 42 , 44 , 48 , 50 , 54 – 59 , 61 – 64 , 68 , 69 , 74 – 85 , 87 – 91 , 93 – 97 , 99 , 100 , 102 , 119 – 122 , 124 , 126 – 128 , 130 – 132 , 135 , 170 , 183 , 184 , 192 ]…”

Section: Resultsunclassified

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Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

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Background Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network–based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

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Section: Resultsunclassified

“…CFS f : 31 [ 48 , 51 , 52 , 65 , 67 , 70 , 72 - 74 , 78 - 81 , 83 , 85 , 86 , 88 , 92 , 94 , 96 , 97 , 101 , 102 , 117 , 130 , 131 , 170 , 171 , 180 , 185 ]…”

Section: Resultsunclassified

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

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“…We then computed various motion-quantification parameters such as velocity, frequency, amplitude, slope, and variance for each movement pattern. We additionally computed jitter and shimmer parameters in accordance with previous research [45]- [47]. Jitter quantifies the variation of the signal waveform along the time axis, while shimmer is associated with amplitude variations.…”

Section: J Feature Extractionmentioning

confidence: 99%

“…Mean absolute difference of the amplitude in decibels [45] Mean absolute value between consecutive amplitudes divided by the mean amplitude len…”

Section: Table 2 List Of Extracted Features From Raw Featuresmentioning

confidence: 99%

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

Shin,

Matsumoto,

Maniruzzaman

et al. 2024

IEEE Access

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P ARKINSON'S disease (PD) is a neurological disorder caused by degeneration of dopaminergic neurons in the midbrain. PD patients mainly suffer from motor symptoms, which significantly impact their daily lives. The diagnostic criteria for PD include the presence of muscle rigidity, tremor, and postural reflex disturbances. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the standard tool for evaluating PD symptoms, part III of which is dedicated to motor symptoms. That part involves a comprehensive set of specific physical examinations, and physicians assign semi quantitative scores from 0 to 4. However, this approach faces notable challenges, including the requirement for movement-disorder experts proficient in using MDS-UPDRS and the presence of substantial inter rater variability even among experts. Overcoming these challenges requires a quantitative and objective

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“…The first approach involves hand-crafting acoustic features, including certain variants of the jitter, shimmer, and harmonic-to-noise ratio that are indicative of PD speech impairments [ 17 , 18 , 19 , 20 , 21 , 22 ] and using traditional machine learning (ML) methods, such as support vector machines (SVM), random forests (RF), k-nearest neighbors (KNN), and regression trees (RT) [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Malekroodi,

Madusanka,

Lee

et al. 2024

Bioengineering

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Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

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Parkinson’s Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 7 publications

References 48 publications

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

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