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Biomedical signal processing

HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS

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Authors:
Yibin Tang, Xufei Li, Ying Chen, Yuan Zhong, Aimin Jiang, Xiaofeng Liu
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14 May 2020 - 10:55pm
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High-accuracy Classification of Attention Deficit Hyperactivity Disorder with L2,1-norm Linear Discriminant Analysis

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[1] Yibin Tang, Xufei Li, Ying Chen, Yuan Zhong, Aimin Jiang, Xiaofeng Liu, "HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5330. Accessed: Jul. 09, 2020.
@article{5330-20,
url = {http://sigport.org/5330},
author = {Yibin Tang; Xufei Li; Ying Chen; Yuan Zhong; Aimin Jiang; Xiaofeng Liu },
publisher = {IEEE SigPort},
title = {HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS},
year = {2020} }
TY - EJOUR
T1 - HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS
AU - Yibin Tang; Xufei Li; Ying Chen; Yuan Zhong; Aimin Jiang; Xiaofeng Liu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5330
ER -
Yibin Tang, Xufei Li, Ying Chen, Yuan Zhong, Aimin Jiang, Xiaofeng Liu. (2020). HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS. IEEE SigPort. http://sigport.org/5330
Yibin Tang, Xufei Li, Ying Chen, Yuan Zhong, Aimin Jiang, Xiaofeng Liu, 2020. HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS. Available at: http://sigport.org/5330.
Yibin Tang, Xufei Li, Ying Chen, Yuan Zhong, Aimin Jiang, Xiaofeng Liu. (2020). "HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS." Web.
1. Yibin Tang, Xufei Li, Ying Chen, Yuan Zhong, Aimin Jiang, Xiaofeng Liu. HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5330

FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE


The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this work, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within the motor imagery classes.

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Authors:
satyam kumar, tharun kumar reddy, vipul arora, laxmidhar behera
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14 May 2020 - 12:45pm
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ICASSP2020_ppt_2541paperId_shortVersionPPT.pdf

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[1] satyam kumar, tharun kumar reddy, vipul arora, laxmidhar behera, "FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5315. Accessed: Jul. 09, 2020.
@article{5315-20,
url = {http://sigport.org/5315},
author = {satyam kumar; tharun kumar reddy; vipul arora; laxmidhar behera },
publisher = {IEEE SigPort},
title = {FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE},
year = {2020} }
TY - EJOUR
T1 - FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE
AU - satyam kumar; tharun kumar reddy; vipul arora; laxmidhar behera
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5315
ER -
satyam kumar, tharun kumar reddy, vipul arora, laxmidhar behera. (2020). FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE. IEEE SigPort. http://sigport.org/5315
satyam kumar, tharun kumar reddy, vipul arora, laxmidhar behera, 2020. FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE. Available at: http://sigport.org/5315.
satyam kumar, tharun kumar reddy, vipul arora, laxmidhar behera. (2020). "FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE." Web.
1. satyam kumar, tharun kumar reddy, vipul arora, laxmidhar behera. FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5315

An LSTM Based Architecture to Relate Speech Stimulus to EEG

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Authors:
Mohammad Jalilpour Monesi, Bernd Accou, Jair Montoya-Martinez, Tom Francart, Hugo Van Hamme
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14 May 2020 - 11:59am
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icassp2020.pdf

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[1] Mohammad Jalilpour Monesi, Bernd Accou, Jair Montoya-Martinez, Tom Francart, Hugo Van Hamme, "An LSTM Based Architecture to Relate Speech Stimulus to EEG", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5311. Accessed: Jul. 09, 2020.
@article{5311-20,
url = {http://sigport.org/5311},
author = {Mohammad Jalilpour Monesi; Bernd Accou; Jair Montoya-Martinez; Tom Francart; Hugo Van Hamme },
publisher = {IEEE SigPort},
title = {An LSTM Based Architecture to Relate Speech Stimulus to EEG},
year = {2020} }
TY - EJOUR
T1 - An LSTM Based Architecture to Relate Speech Stimulus to EEG
AU - Mohammad Jalilpour Monesi; Bernd Accou; Jair Montoya-Martinez; Tom Francart; Hugo Van Hamme
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5311
ER -
Mohammad Jalilpour Monesi, Bernd Accou, Jair Montoya-Martinez, Tom Francart, Hugo Van Hamme. (2020). An LSTM Based Architecture to Relate Speech Stimulus to EEG. IEEE SigPort. http://sigport.org/5311
Mohammad Jalilpour Monesi, Bernd Accou, Jair Montoya-Martinez, Tom Francart, Hugo Van Hamme, 2020. An LSTM Based Architecture to Relate Speech Stimulus to EEG. Available at: http://sigport.org/5311.
Mohammad Jalilpour Monesi, Bernd Accou, Jair Montoya-Martinez, Tom Francart, Hugo Van Hamme. (2020). "An LSTM Based Architecture to Relate Speech Stimulus to EEG." Web.
1. Mohammad Jalilpour Monesi, Bernd Accou, Jair Montoya-Martinez, Tom Francart, Hugo Van Hamme. An LSTM Based Architecture to Relate Speech Stimulus to EEG [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5311

Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation


The signatures of swallowing vary depending on the volume of bolus swallowed. Among existing instrumental methods, cervical auscultation (CA) captures the acoustic signatures of the swallow sound. Although many features present in the literature can characterize volumes of swallow using CA, they require manual annotations of the different components in the sound.

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Authors:
Siddharth Subramani, Achuth Rao MV, Divya Giridhar, Prasanna Suresh Hegde, Prasanta Kumar Ghosh
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15 May 2020 - 1:19am
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Subramani_Presentation_ICASSP_2020.pdf

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[1] Siddharth Subramani, Achuth Rao MV, Divya Giridhar, Prasanna Suresh Hegde, Prasanta Kumar Ghosh, "Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5303. Accessed: Jul. 09, 2020.
@article{5303-20,
url = {http://sigport.org/5303},
author = {Siddharth Subramani; Achuth Rao MV; Divya Giridhar; Prasanna Suresh Hegde; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation},
year = {2020} }
TY - EJOUR
T1 - Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation
AU - Siddharth Subramani; Achuth Rao MV; Divya Giridhar; Prasanna Suresh Hegde; Prasanta Kumar Ghosh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5303
ER -
Siddharth Subramani, Achuth Rao MV, Divya Giridhar, Prasanna Suresh Hegde, Prasanta Kumar Ghosh. (2020). Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation. IEEE SigPort. http://sigport.org/5303
Siddharth Subramani, Achuth Rao MV, Divya Giridhar, Prasanna Suresh Hegde, Prasanta Kumar Ghosh, 2020. Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation. Available at: http://sigport.org/5303.
Siddharth Subramani, Achuth Rao MV, Divya Giridhar, Prasanna Suresh Hegde, Prasanta Kumar Ghosh. (2020). "Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation." Web.
1. Siddharth Subramani, Achuth Rao MV, Divya Giridhar, Prasanna Suresh Hegde, Prasanta Kumar Ghosh. Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5303

Mental Fatigue Prediction from Multi-Channel ECoG Signal


Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two non-human primates (NHPs) as they performed a cognitively demanding task over extended periods of time. We employed a set of biomarkers to identify mental fatigue and a gradient boosting classifier to predict the performance outcome, seconds prior to the actual behavior response.

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Authors:
Lin Yao, Jonathan Baker, Jae-Wook Ryou, Nicholas Schiff, Keith Purpura
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14 May 2020 - 3:08am
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[1] Lin Yao, Jonathan Baker, Jae-Wook Ryou, Nicholas Schiff, Keith Purpura, "Mental Fatigue Prediction from Multi-Channel ECoG Signal", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5243. Accessed: Jul. 09, 2020.
@article{5243-20,
url = {http://sigport.org/5243},
author = {Lin Yao; Jonathan Baker; Jae-Wook Ryou; Nicholas Schiff; Keith Purpura },
publisher = {IEEE SigPort},
title = {Mental Fatigue Prediction from Multi-Channel ECoG Signal},
year = {2020} }
TY - EJOUR
T1 - Mental Fatigue Prediction from Multi-Channel ECoG Signal
AU - Lin Yao; Jonathan Baker; Jae-Wook Ryou; Nicholas Schiff; Keith Purpura
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5243
ER -
Lin Yao, Jonathan Baker, Jae-Wook Ryou, Nicholas Schiff, Keith Purpura. (2020). Mental Fatigue Prediction from Multi-Channel ECoG Signal. IEEE SigPort. http://sigport.org/5243
Lin Yao, Jonathan Baker, Jae-Wook Ryou, Nicholas Schiff, Keith Purpura, 2020. Mental Fatigue Prediction from Multi-Channel ECoG Signal. Available at: http://sigport.org/5243.
Lin Yao, Jonathan Baker, Jae-Wook Ryou, Nicholas Schiff, Keith Purpura. (2020). "Mental Fatigue Prediction from Multi-Channel ECoG Signal." Web.
1. Lin Yao, Jonathan Baker, Jae-Wook Ryou, Nicholas Schiff, Keith Purpura. Mental Fatigue Prediction from Multi-Channel ECoG Signal [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5243

ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION

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Authors:
Danyang Wang, Yuchun Fang* , Yifan Li, Changfeng Chai
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13 May 2020 - 11:07pm
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poster.pdf

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[1] Danyang Wang, Yuchun Fang* , Yifan Li, Changfeng Chai, "ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5200. Accessed: Jul. 09, 2020.
@article{5200-20,
url = {http://sigport.org/5200},
author = {Danyang Wang; Yuchun Fang* ; Yifan Li; Changfeng Chai },
publisher = {IEEE SigPort},
title = {ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION},
year = {2020} }
TY - EJOUR
T1 - ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION
AU - Danyang Wang; Yuchun Fang* ; Yifan Li; Changfeng Chai
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5200
ER -
Danyang Wang, Yuchun Fang* , Yifan Li, Changfeng Chai. (2020). ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION. IEEE SigPort. http://sigport.org/5200
Danyang Wang, Yuchun Fang* , Yifan Li, Changfeng Chai, 2020. ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION. Available at: http://sigport.org/5200.
Danyang Wang, Yuchun Fang* , Yifan Li, Changfeng Chai. (2020). "ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION." Web.
1. Danyang Wang, Yuchun Fang* , Yifan Li, Changfeng Chai. ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5200

Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG


An emerging research direction considers the inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) to bring about the synergy between the easy measurability of PPG and the rich clinical knowledge of ECG to facilitate preventive healthcare. Previous reconstruction using a universal basis has limited accuracy due to the lack of rich representative power. This paper proposes a cross-domain joint dictionary learning (XDJDL) framework to maximize the expressive power for the two cross-domain signals.

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Authors:
Xin Tian, Qiang Zhu, Yuenan Li, Min Wu
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20 June 2020 - 5:29pm
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Presentation slides of ICASSP 2020 paper: "Cross-domain Joint Dictionary Learning (XDJDL) for ECG Reconstruction from PPG"

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[1] Xin Tian, Qiang Zhu, Yuenan Li, Min Wu, "Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5187. Accessed: Jul. 09, 2020.
@article{5187-20,
url = {http://sigport.org/5187},
author = {Xin Tian; Qiang Zhu; Yuenan Li; Min Wu },
publisher = {IEEE SigPort},
title = {Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG},
year = {2020} }
TY - EJOUR
T1 - Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG
AU - Xin Tian; Qiang Zhu; Yuenan Li; Min Wu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5187
ER -
Xin Tian, Qiang Zhu, Yuenan Li, Min Wu. (2020). Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG. IEEE SigPort. http://sigport.org/5187
Xin Tian, Qiang Zhu, Yuenan Li, Min Wu, 2020. Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG. Available at: http://sigport.org/5187.
Xin Tian, Qiang Zhu, Yuenan Li, Min Wu. (2020). "Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG." Web.
1. Xin Tian, Qiang Zhu, Yuenan Li, Min Wu. Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5187

Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides

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Authors:
Mircea Moscu, Ricardo Borsoi, Cédric Richard
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13 May 2020 - 4:54pm
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slides for the video presentation

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[1] Mircea Moscu, Ricardo Borsoi, Cédric Richard, "Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5141. Accessed: Jul. 09, 2020.
@article{5141-20,
url = {http://sigport.org/5141},
author = {Mircea Moscu; Ricardo Borsoi; Cédric Richard },
publisher = {IEEE SigPort},
title = {Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides},
year = {2020} }
TY - EJOUR
T1 - Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides
AU - Mircea Moscu; Ricardo Borsoi; Cédric Richard
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5141
ER -
Mircea Moscu, Ricardo Borsoi, Cédric Richard. (2020). Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides. IEEE SigPort. http://sigport.org/5141
Mircea Moscu, Ricardo Borsoi, Cédric Richard, 2020. Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides. Available at: http://sigport.org/5141.
Mircea Moscu, Ricardo Borsoi, Cédric Richard. (2020). "Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides." Web.
1. Mircea Moscu, Ricardo Borsoi, Cédric Richard. Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5141

Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network


Blood pressure (BP) is a vital sign of the human body and an important parameter for early detection of cardiovascular diseases. It is usually measured using cuff-based devices or monitored invasively in critically-ill patients. This paper presents two techniques that enable continuous and noninvasive cuff-less BP estimation using photoplethysmography (PPG) signals with Convolutional Neural Networks (CNNs). The first technique is calibration-free.

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Authors:
Oded Schlesinger, Nitai Vigderhouse, Danny Eytan, Yair Moshe
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13 May 2020 - 4:48pm
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ICASSP Presentation - Blood Pressure Estimation from PPG Signals.pdf

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[1] Oded Schlesinger, Nitai Vigderhouse, Danny Eytan, Yair Moshe, "Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5138. Accessed: Jul. 09, 2020.
@article{5138-20,
url = {http://sigport.org/5138},
author = {Oded Schlesinger; Nitai Vigderhouse; Danny Eytan; Yair Moshe },
publisher = {IEEE SigPort},
title = {Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network},
year = {2020} }
TY - EJOUR
T1 - Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network
AU - Oded Schlesinger; Nitai Vigderhouse; Danny Eytan; Yair Moshe
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5138
ER -
Oded Schlesinger, Nitai Vigderhouse, Danny Eytan, Yair Moshe. (2020). Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network. IEEE SigPort. http://sigport.org/5138
Oded Schlesinger, Nitai Vigderhouse, Danny Eytan, Yair Moshe, 2020. Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network. Available at: http://sigport.org/5138.
Oded Schlesinger, Nitai Vigderhouse, Danny Eytan, Yair Moshe. (2020). "Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network." Web.
1. Oded Schlesinger, Nitai Vigderhouse, Danny Eytan, Yair Moshe. Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5138

Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion


Pathological Hand Tremor (PHT) is one of the most prevalent symptoms of some neurological movement disorders such as Parkinson’s Disease (PD) and Essential Tremor (ET). Characterization, estimation, and extraction of PHT is a crucial requirement for assistive and robotic rehabilitation technologies that aim to counteract or resist PHT as an input noise to the system. In general, research in the literature on the topic of PHT removal can be categorized into two major categories, namely, classic and data-driven methods.

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Authors:
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi
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8 November 2019 - 7:28pm
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Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion

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[1] Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi, "Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4935. Accessed: Jul. 09, 2020.
@article{4935-19,
url = {http://sigport.org/4935},
author = {Soroosh Shahtalebi; S. Farokh Atashzar; Rajni V. Patel; Arash Mohammadi },
publisher = {IEEE SigPort},
title = {Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion},
year = {2019} }
TY - EJOUR
T1 - Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion
AU - Soroosh Shahtalebi; S. Farokh Atashzar; Rajni V. Patel; Arash Mohammadi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4935
ER -
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi. (2019). Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion. IEEE SigPort. http://sigport.org/4935
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi, 2019. Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion. Available at: http://sigport.org/4935.
Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi. (2019). "Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion." Web.
1. Soroosh Shahtalebi, S. Farokh Atashzar, Rajni V. Patel, Arash Mohammadi. Training of Deep Bidirectional RNNs for Hand Motion Filtering via Multimodal Data Fusion [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4935

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