Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, in...Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting optimized features from all extracted features. Afterwards a network based on support vector machine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkinson’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, optimized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best classification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5).展开更多
Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of ...Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of the biomarkers that enable the detection of Parkinson’s disease affection. Numerous researches are based on recordings from controlled environments;nonetheless fewer apply real circumstances. In the present study, three objectives were examined: recording fragmentation (paragraph, sentences, time-based), variable encodings (Pulse-Code Modulation [PCM], GSM-Full Rate [FR], G.723.1) and majority voting on 8 kHz records using multiple classifiers. Support Vector Machine (SVM), Long Short-Term Memory (LSTM), i-vector and x-vector classifiers were evaluated in contrast with SVM as baseline. The highest results in accuracy and F1-score were achieved using i-vector models. Although variable encodings generally caused decrease in Parkinson-disease recognition, decline was within 2% - 3% at best. Moreover, fragmentation did not yield a clear outcome though some classifiers performed with the very similar efficiency along the differently fragmented sets. Majority voting did produce a slight increase in classification performance compared to as if no aggregation is used.展开更多
Common neurodegenerative diseases of the central nervous system are characterized by progressive damage to the function of neurons, even leading to the permanent loss of function. Gene therapy via gene replacement or ...Common neurodegenerative diseases of the central nervous system are characterized by progressive damage to the function of neurons, even leading to the permanent loss of function. Gene therapy via gene replacement or gene correction provides the potential for transformative therapies to delay or possibly stop further progression of the neurodegenerative disease in affected patients. Adeno-associated virus has been the vector of choice in recent clinical trials of therapies for neurodegenerative diseases due to its safety and efficiency in mediating gene transfer to the central nervous system. This review aims to discuss and summarize the progress and clinical applications of adeno-associated virus in neurodegenerative disease in central nervous system. Results from some clinical trials and successful cases of central neurodegenerative diseases deserve further study and exploration.展开更多
文摘Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting optimized features from all extracted features. Afterwards a network based on support vector machine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkinson’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, optimized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best classification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5).
文摘Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of the biomarkers that enable the detection of Parkinson’s disease affection. Numerous researches are based on recordings from controlled environments;nonetheless fewer apply real circumstances. In the present study, three objectives were examined: recording fragmentation (paragraph, sentences, time-based), variable encodings (Pulse-Code Modulation [PCM], GSM-Full Rate [FR], G.723.1) and majority voting on 8 kHz records using multiple classifiers. Support Vector Machine (SVM), Long Short-Term Memory (LSTM), i-vector and x-vector classifiers were evaluated in contrast with SVM as baseline. The highest results in accuracy and F1-score were achieved using i-vector models. Although variable encodings generally caused decrease in Parkinson-disease recognition, decline was within 2% - 3% at best. Moreover, fragmentation did not yield a clear outcome though some classifiers performed with the very similar efficiency along the differently fragmented sets. Majority voting did produce a slight increase in classification performance compared to as if no aggregation is used.
文摘Common neurodegenerative diseases of the central nervous system are characterized by progressive damage to the function of neurons, even leading to the permanent loss of function. Gene therapy via gene replacement or gene correction provides the potential for transformative therapies to delay or possibly stop further progression of the neurodegenerative disease in affected patients. Adeno-associated virus has been the vector of choice in recent clinical trials of therapies for neurodegenerative diseases due to its safety and efficiency in mediating gene transfer to the central nervous system. This review aims to discuss and summarize the progress and clinical applications of adeno-associated virus in neurodegenerative disease in central nervous system. Results from some clinical trials and successful cases of central neurodegenerative diseases deserve further study and exploration.
