Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease

Zhang, Wei; Yang, Zhuokun; Li, Hantao; Huang, Dong; Wang, Lipeng; Wei, Yanzhao; Zhang, Lei; Ma, Lin; Feng, Hongliang; Pan, Jing; Guo, Yuzhu; Chan, Piu

doi:10.1038/s41597-022-01713-8

Cited by 18 publications

(17 citation statements)

References 15 publications

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“…Accumulating evidence has demonstrated accurate FOG detection and, in some cases prediction, based on different signals, including EEG [50], [52], [53] as well as other physiological signals such as heart-rate [54], [55], suggesting that incorporating neural and physiological dynamics can improve FOG prediction [56], [57]. While the current work did not incorporate additional sensors, future work should evaluate if/the extent to which additional sensors (i.e.…”

Section: B Future Directions and Challengesmentioning

confidence: 99%

Bilateral Leg Stepping Coherence as a Predictor of Freezing of Gait in Patients With Parkinson’s Disease Walking With Wearable Sensors

Krasovsky

Heimler

Koren

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Freezing of Gait (FOG) is among the most debilitating symptoms of Parkinson's Disease (PD), characterized by a sudden inability to generate effective stepping. In preparation for the development of a real-time FOG prediction and intervention device, this work presents a novel FOG prediction algorithm based on detection of altered interlimb coordination of the legs, as measured using two inertial movement sensors and analyzed using a wavelet coherence algorithm. Methods: Fourteen participants with PD (in OFF state) were asked to walk in challenging conditions (e.g. with turning, dual-task walking, etc.) while wearing inertial motion sensors (waist, 2 shanks) and being videotaped. Occasionally, participants were asked to voluntarily stop (VOL). FOG and VOL events were identified by trained researchers based on videos. Wavelet analysis was performed on shank sagittal velocity signals and a synchronization loss threshold (SLT) was defined and compared between FOG and VOL. A proof-of-concept analysis was performed for a subset of the data to obtain preliminary classification characteristics of the novel measure. Results: 128 FOG and 42 VOL episodes were analyzed. SLT occurred earlier for FOG (MED=1.81 sec prior to stop, IQR=1.57) than for VOL events (MED=0.22 sec, IQR=0.76) (Z=-4.3, p<0.001, ES=1.15). These time differences were not related with measures of disease severity. Preliminary results demonstrate sensitivity of 98%, specificity of 42% (mostly due to 'turns' detection) and balanced accuracy of 70% for SLT-based prediction, with good differentiation between FOG and VOL. Conclusions: Wavelet analysis provides a relatively simple, promising approach for prediction of FOG in people with PD.

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Section: B Future Directions and Challengesmentioning

confidence: 99%

Bilateral Leg Stepping Coherence as a Predictor of Freezing of Gait in Patients With Parkinson’s Disease Walking With Wearable Sensors

Krasovsky

Heimler

Koren

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…There are 4 studies related to AD and 7 studies related to PD. The most common combination of data was movement data and speech (n = 5 studies) with one study focusing on AD [ 47 ] and 4 on PD [ 29 – 31 , 34 ], followed by movement data and EEG signals (n = 3 studies, with 1 study in AD and 2 in PD) [ 26 , 55 , 66 ].…”

Section: Narrative Synthesis Of Relevant Findings From the Evidencementioning

confidence: 99%

“…Two AD related studies [ 47 , 55 ] and 4 PD [ 29 , 30 , 32 , 66 ] studies used gait (walking) movement data. Generally, gait data was collected whilst participants walked at their usual pace over nine meters with a marker-based motion capture system (camera or sensor-based).…”

Section: Narrative Synthesis Of Relevant Findings From the Evidencementioning

confidence: 99%

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review

Huang

Bai

et al. 2023

Health Inf Sci Syst

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With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer’s disease (AD) and Parkinson’s disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls; the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small sample sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.

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“…The data adopted the Beihang-Xuanwu multimodal freezing of gait dataset [26]. The data collection was completed in Xuanwu Hospital Affiliated to Capital Medical University (ethical approval number: 2019-014).…”

Section: A Materialsmentioning

confidence: 99%

“…EEG signals were recorded using a 32-channel wireless system MOVE (BRAIN PRODUCTS, Germany). A total of 25 EEG channels were acquired for this experiment, and the specific channels measured refer to [26].…”

Section: A Materialsmentioning

confidence: 99%

EEG Detection and Prediction of Freezing of Gait in Parkinson’s Disease Based on Spatiotemporal Coherent Modes

Li¹,

Guo²

2023

Preprint

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<p>Freezing of gait (FOG) in the Parkinson’s disease has a complex mechanism and is closely related brain activities. Timely prediction of FOG is crucial to fall prevention and injury avoidance. Traditional electroencephalogram (EEG) processing methods extract time, spatial, frequency, or phase information separately and use them or their combinations, which fragment the connections among these heterogeneous features and cannot completely characterize the whole brain dynamics during the occurrence of FOG. In this study, dynamic spatiotemporal coherent modes during the FOG were studied and the associated FOG detection and prediction were proposed. For capturing the changes of the brain, dynamic mode decomposition (DMD) method was applied. Dynamic changes of the spatiotemporal modes in both amplitude and phase of motor-related frequency bands were analyzed and an analytic common spatial patterns (ACSP) was used as a spatial filter to extract the essential differences among the normal, freezing and transitional gaits. The proposed method was verified in practical clinical data. Results showed that in the detection task, the DMD-ACSP achieved an accuracy of 89.1 ± 3.6% and sensitivity of 83.5 ± 4.3%, respectively. In the prediction task, an 83.5 ± 3.2% accuracy and 86.7 ± 7.8% sensitivity were achieved. Comparative studies showed that the DMD-ACSP method significantly improves the FOG detection and prediction performance. Moreover, the DMD-ACSP reveals the spatial patterns of dynamic brain functional connectivity which best discriminate the different gaits. The spatiotemporal coherent modes may provide useful indication for transcranial magnetic stimulation neuromodulation in medical practices. </p>

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Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease

Cited by 18 publications

References 15 publications

Bilateral Leg Stepping Coherence as a Predictor of Freezing of Gait in Patients With Parkinson’s Disease Walking With Wearable Sensors

Bilateral Leg Stepping Coherence as a Predictor of Freezing of Gait in Patients With Parkinson’s Disease Walking With Wearable Sensors

Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review

EEG Detection and Prediction of Freezing of Gait in Parkinson’s Disease Based on Spatiotemporal Coherent Modes

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