Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

Poorjam, Amir Hossein; Kavalekalam, Mathew Shaji; Shi, Liming; Raykov, Yordan P.; Jensen, Jesper Rindom; Little, Max A.; Christensen, Mads Græsbøll

doi:10.1016/j.specom.2020.12.007

Cited by 15 publications

(11 citation statements)

References 66 publications

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“…To remove noise, we plan to augment the techniques proposed in Poorjam et al [ 56 ] to automatically enhance our data quality by detecting the segments of data that conform to our experimental design. Moreover, as discussed above, gender and age can appear as confounding variables in the PD prediction task.…”

Section: Discussionmentioning

confidence: 99%

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Rahman¹,

Lee²,

Islam³

et al. 2021

J Med Internet Res

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Background Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases—fueled mostly by environmental pollution and an aging population—can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. Objective In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. Methods We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, “the quick brown fox jumps over the lazy dog.” We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning–based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model’s output. Results We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost—a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing “ahh”) influence the model’s decision the most. Conclusions Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

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Section: Discussionmentioning

confidence: 99%

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Rahman¹,

Lee²,

Islam³

et al. 2021

J Med Internet Res

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“…The performance indicator of the selected algorithm plays a crucial role in bias estimation. Even though accuracy is good, there are chances of the existence of bias or inclusion bias due to the non-clinical validation of AI-based predictions [ 46 , 47 , 48 ].…”

Section: Search Strategy and Statistical Distributionmentioning

confidence: 99%

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

et al. 2022

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Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

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“…Its ability to detect small changes in voice features is better than speech therapists or audiologists. [ 25 ] The possibility of discriminative assessment among patients with PD, progressive supranuclear palsy, and multiple system atrophy has been reported. [ 23 ] The selection of adequate voice features and MLAs make voice a possible biomarker for PD diagnosis.…”

Section: Achine L Earning a Lgo...mentioning

confidence: 99%

“…[ 29 ] Some challenges to remote assessment using smartphones include degraded voice quality caused by noise, reverberation, and other non-linear distortion. [ 25 ] Speech enhancement techniques can improve voice quality and, therefore, PD classification accuracy. [ 25 ]…”

Section: U Se Of M Las In the D...mentioning

confidence: 99%

Management of Parkinson's Disease Dysarthria

Kumar

Tripathy

Kumar

et al. 2022

Annals of Indian Academy of Neurology

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Speech disorder is a significant problem for people affected with Parkinson's disease (PD) leading to a substantial disability to communicate with others. PD affects the voice, including changes in pitch, intensity, articulation, and syllable rate.We aimed to study the current status of artificial intelligence (AI) using machine learning algorithms (MLAs) in the assessment of speech abnormalities in PD along with the generation of intelligible synthetic speech for voice rehabilitation. We searched the literature for studies focusing on speech/voice disorder in PD and rehabilitation techniques till June 18, 2022. We searched PubMed and Engineering Village (Compendex and Inspec combined) databases. After careful screening of the title and evaluation of abstracts, we used select articles describing the use of AI or its various forms in the management of speech abnormalities in PD to synthesize this review. MLAs classify PD and non-PD patients with an accuracy of more than 90% using only voice features. Non-acoustic sensors can rehabilitate PD patient by converting dysarthric speech to highly intelligible speech using MLAs. MLAs can automatically assess several speech features and quantify the progression of speech abnormalities in PD. PD speech rehabilitation using MLAs may prove superior to other available therapies.

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Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

Cited by 15 publications

References 66 publications

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

Management of Parkinson's Disease Dysarthria

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