Deep learning for Parkinson's disease: a case study on Freezing of Gait

We propose a deep-learning method for feature extraction from gait data of Parkinson’s disease patients. Our goal is to verify whether a fine classification of gait between similar groups can be achieved. To this end, we refer as a case study to the Freezing of Gait (FOG), and we measure gait data from two groups of patients, which exhibit (respectively, do not exhibit) this symptom. Wearable inertial sensors are employed, and data are collected during activities similar to those performed by patients during their daily living. Moreover, most patients are in daily on state, hence the two groups are difficult to classify, as their gait does not exhibit evident differences. Whereas classical Machine Learning methods are not sufficiently robust to perform such a fine classification, if they are fed with features extracted by means of a deep network, the results are satisfactory also when a large dataset is not available and data present a mild degree of heterogeneit

Detection of freezing of gait in people withParkinson’s disease using smartphones

Freezing of Gait (FOG) is one of the most trouble-some motor symptoms associated with Parkinson’s disease (PD),characterised by brief episodes of inability to step. It involvesincreased risk of falls and reduced quality of life, and correlateswith motor fluctuations and progression of the disease. Hence, theknowledge of FOG event frequency, duration, daily distributionand response to drug therapy is fundamental for a reliablepatient’s assessment. In this study, we propose a FOG detectionalgorithm that takes as input inertial data from a single waist-mounted smartphone, and provides information about presenceand duration of FOG episodes. Data acquisition was carried on38 PD patients and 21 elderly subjects executing a standard6-minute walking test. More than 3.5 hours of accelerationdata have been collected. A combination of Support VectorMachine and k-Nearest Neighbour classifiers has been designed.Sensitivity of 95.4%, specificity of 98.8%, precision of 92.8%and accuracy of 98.3% in the 10-fold cross validation, and adetection rate of 84% in Leave-one-Subject-Out validation were obtained. These results, along with a good time resolution in theFOG duration identification and very efficient processing times,make the algorithm a promising tool for reliable FOG assessmentduring activities of daily livin

Single-channel EEG classification of sleep stages based on REM microstructure

Rapid-eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night-time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single-channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K-nearest neighbour (K-NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F-1 score (REM class) of about 0.83 (RF), 0.80 (K-NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single-channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels

An algorithm for Parkinson's disease speech classification based on isolated words analysis

Introduction Automatic assessment of speech impairment is a cutting edge topic in Parkinson's disease (PD). Language disorders are known to occur several years earlier than typical motor symptoms, thus speech analysis may contribute to the early diagnosis of the disease. Moreover, the remote monitoring of dysphonia could allow achieving an effective follow-up of PD clinical condition, possibly performed in the home environment. Methods In this work, we performed a multi-level analysis, progressively combining features extracted from the entire signal, the voiced segments, and the on-set/off-set regions, leading to a total number of 126 features. Furthermore, we compared the performance of early and late feature fusion schemes, aiming to identify the best model configuration and taking advantage of having 25 isolated words pronounced by each subject. We employed data from the PC-GITA database (50 healthy controls and 50 PD patients) for validation and testing. Results We implemented an optimized k-Nearest Neighbours model for the binary classification of PD patients versus healthy controls. We achieved an accuracy of 99.4% in 10-fold cross-validation and 94.3% in testing on the PC-GITA database (average value of male and female subjects). Conclusion The promising performance yielded by our model confirms the feasibility of automatic assessment of PD using voice recordings. Moreover, a post-hoc analysis of the most relevant features discloses the option of voice processing using a simple smartphone application

Prediction of Freezing of Gait in Parkinson’s Disease using Wearables and Machine Learning

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-onesubject- out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness

Smartphone-Based Evaluation of Postural Stability in Parkinson’s Disease Patients During Quiet Stance

Background: Postural instability is one of the most troublesome motor symptoms of Parkinson’s Disease(PD).It impairs patients’quality of life and results in high risk of falls. The aim of this study is to provide a reliable tool for the automated assessment of postural instability. Methods: Data acquisition was performed on 42 PD patients and 7 young healthy subjects. They were asked to keep a quiet stance position for at least 30 s while wearing a waist-mounted smartphone. A total number of 414 features was extracted from both time and frequency domain, selected based on Pearson’s correlation, and fed to an optimized Support Vector Machine. Results: The implemented model was able to differentiate patients with mild postural instability from those with severe postural instability and from healthy controls, with 100% accuracy. Conclusion: This study demonstrated the feasibility of using inertial sensors embedded in commercial smartphones and proposed a simple protocol for accurate postural instability scoring. This tool can be used for early detection of PD motor signs, disease follow-up and fall prevention

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease

Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease

Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living

Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review

Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment

A new index to assess turning quality and postural stability in patients with Parkinson's disease

Parkinson's disease is a neuro-degenerative disorder characterized by the progressive death of dopamine neurons. This leads to delayed and uncoordinated movements, and impacts on the patients’ motor performance with reduced movement intensity, increased axial rigidity and impaired cadence regulation. Turning provides privileged insights in postural instability and fall prediction, as it is regularly performed during daily activities, requires multi-limb coordination. The objective of this work was to define a Quality of Movement (QoM) index, inferred from inertial data related to turns, and strictly correlated with the patient's motor conditions, postural stability, and stage of the disease. Such a concise representation finds its main application in the remote monitoring of patients during daily activities at home. We have recorded and analyzed 180° turns in 72 patients, using inertial sensors embedded in the smartphone. We have set up an algorithm for binary classification of patients: mild vs. moderate/severe conditions, according to the Hoehn and Yahr scale of disease progression and disability degree. Our QoM index is defined as the a posteriori probability output by this binary classifier. It exhibits high correlation (r = 0.73) with the clinical score of postural stability, as well as with the average of four clinical scores related to movement impairment (r = 0.75). These results, together with the widespread smartphone use, provide a step in the direction of a practical, objective and reliable tool for PD patients remote monitoring in domestic environment