Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks
暂无分享,去创建一个
Liang Zhao | Yu Zhang | Yu Bao | Ruidong Ye | Aijuan Zhang | Liang Zhao | Ruidong Ye | Yu Zhang | Yu Bao | Aijuan Zhang
[1] Pu Wang,et al. LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification , 2020, IEEE Transactions on Instrumentation and Measurement.
[2] Gavin D Perkins,et al. Compression feedback devices over estimate chest compression depth when performed on a bed. , 2009, Resuscitation.
[3] Benoit Gosselin,et al. A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning , 2019, IEEE Transactions on Biomedical Circuits and Systems.
[4] Yuejin Zhao,et al. Feature extraction and classification of heart sound using 1D convolutional neural networks , 2019, EURASIP Journal on Advances in Signal Processing.
[5] Behrooz Lotfi,et al. An approach for velocity and position estimation through acceleration measurements , 2016 .
[6] Guillaume-Alexandre Bilodeau,et al. Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait , 2019, Expert Syst. Appl..
[7] Patrick Cardinal,et al. End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network , 2019, Expert Syst. Appl..
[8] Paavo Alku,et al. Analysis and Detection of Pathological Voice Using Glottal Source Features , 2020, IEEE Journal of Selected Topics in Signal Processing.
[9] Yongqi Chang,et al. A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks , 2020, Sensors.
[10] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[11] Ahmet Kondoz,et al. Radio Frequency Traffic Classification Over WLAN , 2017, IEEE/ACM Transactions on Networking.
[12] Rodney G. Vaughan,et al. Deep Learning Radar Design for Breathing and Fall Detection , 2020, IEEE Sensors Journal.
[13] C Scheffer,et al. A modeling approach to the effects of force guided versus depth guided compression during cardiopulmonary resuscitation on different chests and back support surfaces. , 2013, Resuscitation.
[14] Mubarak Shah,et al. Norm-Preservation: Why Residual Networks Can Become Extremely Deep? , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Pierre Jean Arnoux,et al. Using an inertial navigation algorithm and accelerometer to monitor chest compression depth during cardiopulmonary resuscitation. , 2016, Medical engineering & physics.
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] Yongfeng Huang,et al. CSFL: A novel unsupervised convolution neural network approach for visual pattern classification , 2017, AI Communications.
[18] Ichinosuke Maeda,et al. Real-time feedback of chest compressions using a flexible pressure sensor. , 2016, Resuscitation.
[19] Stan Szpakowicz,et al. Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation , 2006, Australian Conference on Artificial Intelligence.
[20] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[21] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[22] W. Haverkamp,et al. Performance of chest compressions with the use of a new audio-visual feedback device: a randomized manikin study in health care professionals. , 2015, Resuscitation.
[23] Paavo Alku,et al. Glottal Source Information for Pathological Voice Detection , 2020, IEEE Access.
[24] Min Hong,et al. Deep Learning in Physiological Signal Data: A Survey , 2020, Sensors.
[25] Yeongtak Song,et al. A new chest compression depth feedback algorithm for high-quality CPR based on smartphone. , 2015, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.
[26] Tae Wook Kim,et al. Novel Chest Compression Depth Measurement Sensor Using IR-UWB for Improving Quality of Cardiopulmonary Resuscitation , 2017, IEEE Sensors Journal.