A Machine Learning Approach to Detecting of Freezing of Gait in Parkinson's Disease Patients

Xia, Yi; Yao, Zhiming; Lu, Yixiang; Zhang, Dexiang; Cheng, Nan

doi:10.1166/jmihi.2018.2379

Cited by 11 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, several attempts have been made to design decision support systems for differential diagnosis of PD in recent years. These includes speech assessment [21][22][23][24][25][26][27], gait monitoring [28][29][30][31][32] or tremor assessment [33,34]. There are several challenges involved in these methods i.e.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Naseer¹,

Rani²,

Naz³

et al. 2019

Neural Comput & Applic

130

View full text Add to dashboard Cite

Parkinson's disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life, however, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson's disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that helps as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that proposed approach provides better detection of Parkinson's disease as compared to state of the art work.

show abstract

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Naseer¹,

Rani²,

Naz³

et al. 2019

Neural Comput & Applic

130

View full text Add to dashboard Cite

show abstract

“…Concerning FOG detection, our algorithm yielded high performance in the recognition of FOG episodes, comparable with those previously described [44,45]. Additionally, in line with previous research [14], our algorithm detected FOG episodes in PD patients on and off therapy with a similar sensitivity, thereby suggesting that L-dopa does not significantly change FOG-related features, but only impacts on the frequency and duration of FOG episodes.…”

Section: Discussionsupporting

confidence: 87%

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

Borzì

Mazzetta

Zampogna

et al. 2021

Sensors

View full text Add to dashboard Cite

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-one-subject-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.

show abstract

“…Many time- and frequency-domain features were computed, as suggested in the literature on FOG prediction and detection [ 15 , 22 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 34 , 35 ]. The features extracted from the inertial signals measured at the leg were calculated on the three components independently (i.e., vertical, medial-lateral and antero-posterior).…”

Section: Methodsmentioning

confidence: 99%

“…Inertial sensors. Single or multiple sensors have been placed on several body segments (e.g., legs [ 28 ], wrist [ 29 ], waist [ 30 ]) for FOG detection [ 29 , 31 ] and prediction [ 28 , 29 , 32 ]. In particular, accelerometers are widely employed, due to their low energy consumption and cost (in particular, those embedded in smartphones [ 15 , 33 ]).…”

Section: Introductionmentioning

confidence: 99%

“…Significant information is provided by the Freeze Index , defined as the ratio between the power contained in the so-called freeze band 3–8 Hz [ 34 ] and that in the locomotion band 0.5–3 Hz [ 29 , 35 ]. Entropy and statistical parameters such as mean value, standard deviation and variance are other sensible metrics [ 28 , 31 ]. Several features, extracted from both the time and frequency domain and different machine learning (ML) algorithms have been employed to classify FOG and pre-FOG events [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Mesin

Porcu

Russu

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

A Machine Learning Approach to Detecting of Freezing of Gait in Parkinson's Disease Patients

Cited by 11 publications

References 0 publications

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

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

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

Contact Info

Product

Resources

About