This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state. We propose the use of recurrent neural networks and exploit recent advancements in attention based learning to segment the PCG signal. This allows the network to identify the most salient aspects of the signal and disregard uninformative information. The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings. Furthermore, we empirically analyse different feature combinations including envelop features, wavelet and Mel Frequency Cepstral Coefficients (MFCC), and provide quantitative measurements that explore the importance of different features in the proposed approach. We demonstrate that a recurrent neural network coupled with attention mechanisms can effectively learn from irregular and noisy PCG recordings. Our analysis of different feature combinations shows that MFCC features and their derivatives offer the best performance compared to classical wavelet and envelop features. Heart sound segmentation is a crucial pre-processing step for many diagnostic applications. The proposed method provides a cost effective alternative to labour extensive manual segmentation, and provides a more accurate segmentation than existing methods. As such, it can improve the performance of further analysis including the detection of murmurs and ejection clicks. The proposed method is also applicable for detection and segmentation of other one dimensional biomedical signals.
Chinese word segmentation (CWS) is the basic of Chinese natural language processing (NLP). The quality of word segmentation will directly affect the rest of NLP tasks. Recently, with the artificial intelligence tide rising again, Long Short-Term Memory (LSTM) neural network, as one of easily modeling in sequence, has been widely utilized in various kinds of NLP tasks, and functions well. Attention mechanism is an ingenious method to solve the memory compression problem on LSTM. Furthermore, inspired by the powerful abilities of bidirectional LSTM models for modeling sequence and CRF model for …
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；University of Kentucky（肯塔基大学）
As the cornerstone of other important technologies, such as speech recognition and speech synthesis, speech enhancement is a critical area in audio signal processing. In this paper, a new deep learning structure for speech enhancement is demonstrated. The model introduces a "full" attention mechanism to a bidirectional sequence-to-sequence method to make use of latent information after each focal frame. This is an extension of the previous attention-based RNN method. The proposed bidirectional attention-based architecture achieves better performance in terms of speech quality (PESQ), compared …
University of Canberra（堪培拉大学）；National Autonomous University of Mexico（墨西哥国立自治大学）；Taipei Medical University Hospital（台北医学大学附属医院）；Taipei Medical University-Shuang Ho Hospital（台北医学大学双和医院）；D Global Biotech Inc.（3D环球生物科技公司）
Assessing pain in patients unable to speak (also called non-verbal patients) is extremely complicated and often is done by clinical judgement. However, this method is not reliable since patients vital signs can fluctuate significantly due to other underlying medical conditions. No objective diagnosis test exists to date that can assist medical practitioners in the diagnosis of pain. In this study we propose the use of functional near-infrared spectroscopy (fNIRS) and deep learning for the assessment of human pain. The aim of this study is to explore the use deep learning to automatically learn…
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks
College of Information Science & Technology, Beijing University of Chemical Technology（北京化工大学信息科学与技术学院）；National Research Center for High-Performance Computing Engineering Technology, Sugon Information Industry Co., Ltd（曙光信息产业股份有限公司高性能计算工程技术国家研究中心）；Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University（西南交通大学信息编码与传输四川省重点实验室）；Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）
A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention
Illinois Institute of Technology（伊利诺伊理工大学）；Fordham University（福特汉姆大学）；Northeastern University（东北大学）；University of Rochester（罗切斯特大学）；University of Saskatchewan（萨斯喀彻温大学）
Recording Brain Activity While Listening to Music Using Wearable EEG Devices Combined with Bidirectional Long Short-Term Memory Networks
School of Music, Jiangxi Normal University（江西师范大学音乐学院）；School of Electronic Information, HuZhou College（湖州学院电子信息学院）；San Francisco State University（旧金山州立大学）
Temperature monitoring is critical for electrical motors to determine if device protection measures should be executed. However, the complexity of the internal structure of Permanent Magnet Synchronous Motors (PMSM) makes the direct temperature measurement of the internal components difficult. This work pragmatically develops three deep learning models to estimate the PMSMs' internal temperature based on readily measurable external quantities. The proposed supervised learning models exploit Long Short-Term Memory (LSTM) modules, bidirectional LSTM, and attention mechanism to form encoder-decod…
COMSATS University Islamabad（COMSATS伊斯兰堡大学）；Tsinghua University（清华大学）；Arab Open University-Palestine（阿拉伯开放大学巴勒斯坦分校）；Jashore University of Science and Technology（杰索尔科技大学）
Attention based Bidirectional GRU hybrid model for inappropriate content detection in Urdu language
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（国家科学技术大学电气与计算机科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
University of Isfahan（伊斯法罕大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；New Jersey Institute of Technology（新泽西理工学院）；Rutgers University（罗格斯大学）
Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to heart stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper in…
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
Faculty of Computer Engineering, K. N. Toosi University of Technology（法尔科夫计算机工程学院，卡·努·托西技术大学）；School of Electrical and Computer Engineering, University of Tehran（电气与计算机工程学院，德黑兰大学）
Facultad de Ingeniería, Universidad de Buenos Aires（布宜诺斯艾利斯大学工程学院）；Instituto Argentino de Matemática ”Alberto P. Calderón”, CONICET（国家科学技术研究委员会“阿尔贝托·P·卡尔德隆”阿根廷数学研究所）
In this work, a novel stack of well-known technologies is presented to determine an automatic method to segment the heart sounds in a phonocardiogram (PCG). We will show a deep recurrent neural network (DRNN) capable of segmenting a PCG into its main components and a very specific way of extracting instantaneous frequency that will play an important role in the training and testing of the proposed model. More specifically, it involves a Long Short-Term Memory (LSTM) neural network accompanied by the Fourier Synchrosqueezed Transform (FSST) used to extract instantaneous time-frequency features …
Bidirectional recurrent neural networks (bi-RNNs), in particular, bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models have recently attracted attention for nonlinearity mitigation in fiber-optic communication. The recently adopted approaches based on these models, however, incur a high computational complexity which may impede their real-time functioning. In this paper, by addressing the sources of complexity in these methods, we propose a more efficient network architecture, where a convolutional neural network encoder and a uni…
