Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals

Yang, Yuzhe; Yuan, Yuan; Zhang, Guo; Wang, Hao; Chen, Ying-Cong; Liu, Yingcheng; Tarolli, Christopher G.; Crepeau, Daniel; Bukartyk, Jan; Junna, Mithri R.; Videnović, Aleksandar; Ellis, Terry; Lipford, Melissa C.; Dorsey, Ray; Katabi, Dina

doi:10.1038/s41591-022-01932-x

Cited by 124 publications

(69 citation statements)

References 54 publications

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“…At the time of the writing, the radio device used in this paper is incorporated in a large number of clinical studies in neurological diseases ( 77 , 78 ), immune diseases ( 79 , 80 ), and rare diseases ( 75 , 81 ). Some of these studies involve over 250 devices, and many of them run for several years.…”

Section: Methodsmentioning

confidence: 99%

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

et al. 2022

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Parkinson’s disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people’s bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients’ daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

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Section: Methodsmentioning

confidence: 99%

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

et al. 2022

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“…As the RMSE of 6.01 in predicting the total MDS-UPDRS score by our predictive model is lesser than the inherent noise of 8.059 in the instrument itself, it is safe to assume that the performance of the predictive model is acceptable for real-world applications. Moreover, as per the recent study [26][27] in predictive modeling of PD using imaging genetics on a combination of DNA genotyping and neuroimaging, the authors reported an RMSE of 7.82 in predicting MDS-UPDRS-Score.…”

Section: Discussionmentioning

confidence: 68%

Predictive Modelling of Parkinson’s Disease Progression Based on RNA-Sequence with Densely Connected Deep Recurrent Neural Networks

Ahmed

Komeili

Park

2022

Preprint

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The advent of recent high throughput sequencing technologies resulted in an unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson’s disease (PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient’s next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson Progression Marker Initiative (PPMI) includes 423 PD patients with a variable number of visits and 34,682 predictor variables for 4 years. We propose a predictive model based on deep Recurrent Neural Network (RNN) with the addition of dense connections and batch normalization into RNN layers. The results show that the proposed architecture can predict PD progression from high dimensional RNA-seq data with a Root Mean Square Error (RMSE) of 6.0 and rank-order correlation of (r = 0.83, p < 0.0001) between the predicted and actual disease status of PD.

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“…For these behavioral states (e.g., sleep), a significant deviation in the breathing pattern may predict underlying pathological conditions as shown in a recent study on patients with Parkinson’s disease. 47 …”

Section: Discussionmentioning

confidence: 99%

Machine learning-based clustering and classification of mouse behaviors via respiratory patterns

Janke¹,

Zhang²,

Ryu³

et al. 2022

iScience

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Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals

Cited by 124 publications

References 54 publications

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

Predictive Modelling of Parkinson’s Disease Progression Based on RNA-Sequence with Densely Connected Deep Recurrent Neural Networks

Machine learning-based clustering and classification of mouse behaviors via respiratory patterns

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