Summary of Parkinson’s Disease Diagnosis Through Deep Learning: a Novel Lstm-based Approach For Freezing Of Gait Detection, by Aqib Nazir Mir et al.
Parkinson’s Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection
by Aqib Nazir Mir, Iqra Nissar, Mumtaz Ahmed, Sarfaraz Masood, Danish Raza Rizvi
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning architecture based on LSTM networks is proposed to aid in the early detection of Parkinson’s disease, which is characterized by the deterioration of brain function. The model employs the VGRF gait signal dataset from Physionet to distinguish between healthy individuals and those diagnosed with Parkinson’s disease. By eliminating manual feature engineering and capturing prolonged temporal dependencies in gait patterns, this method improves the diagnosis of Parkinson’s disease. The LSTM network resolves vanishing gradients by employing memory blocks and optimizes information assimilation. To prevent overfitting, dropout and L2 regularization techniques are used. The Adam optimizer is employed for the optimization process. The results show that the proposed approach surpasses current state-of-the-art models in FOG episode detection, achieving an accuracy of 97.71%, sensitivity of 99%, precision of 98%, and specificity of 96%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Parkinson’s disease is a neurodegenerative condition that affects brain function. It can be challenging to diagnose early on because people with Parkinson’s may behave similarly to healthy individuals. Researchers have developed a new way to detect Parkinson’s using deep learning, a type of artificial intelligence. They used a special dataset called VGRF gait signal from Physionet to help their model learn. The model is better at detecting signs of Parkinson’s than other methods and can even predict when someone will have a “freezing of gait” episode, which is a common symptom. |
Keywords
» Artificial intelligence » Deep learning » Dropout » Feature engineering » Lstm » Optimization » Overfitting » Precision » Regularization
