Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively.

A Review on Deep Learning Methods for ECG Arrhythmia Classification

This paper presents the essential elements for developing objective methods of assessment of the performance of time-frequency signal analysis techniques. We define a measure for assessing the resolution performance of time-frequency distributions (TFDs) in separating closely spaced components in the time-frequency domain. The measure takes into account key attributes of TFDs, such as components mainlobes and sidelobes and cross-terms. The introduction of this measure allows to quantify the quality of TFDs instead of relying solely on visual inspection of their plots. The method of assessment of performance of TFDs also allows the improvement of methodologies for designing high-resolution quadratic TFDs for time-frequency analysis of multicomponent signals. Different TFDs, including the modified B distribution, are optimized using this methodology. Examples of a performance comparison of quadratic TFDs in resolving closely spaced components in the time-frequency domain, using the proposed resolution measure, are provided.

Resolution Measure Criteria for the Objective Assessment of the Performance of Quadratic Time-Frequency Distributions

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Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects

Personal identification method using the Electrocardiogram (ECG) signal is an active research area since the ECG signal cannot be forged and can be acquired without active awareness by the subject. In this paper, we propose a personal recognition system using the 2-D coupling image of the ECG signal. The proposed system uses the 2-D coupling image generated from three periods of the ECG signal as input data to the network whose design is based on a Convolutional Neural Network (CNN) that is specialized for image processing. Waveform of the 2-D coupling image which is the input data to the network cannot be visually confirmed and it has the advantage of being able to augment the QRS-complex which is a personal unique information. We confirm recognition performance of 99.2% from the experiment result for the proposed personal recognition system using MIT-BIH data.

Personal recognition using convolutional neural network with ECG coupling image

Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data.

https://www.mdpi.com/1424-8220/20/19/5481/pdf

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase.

ECG-based driver inattention identification during naturalistic driving using Mel-frequency cepstrum 2-D transform and convolutional neural networks

Political statements are carefully crafted to garner public support for a particular ideology. These statements are often biased and sometimes misleading. Separating fact from fiction has proven to be a difficult task, generally accomplished by cross-checking political statements against an impartial and trustworthy news source. In this article, we make three contributions. First, we compile a novel multimodal dataset, which consists of 180 videos with accompanying audio recordings and transcripts, featuring 88 politicians categorized by political party. To our knowledge, this is the second multimodal deception detection dataset from real-life data and the first in the political field. Second, we extract features from the linguistic, visual, and acoustic modalities to develop a system capable of discriminating between truthful and deceptive political statements. Finally, we perform an extensive analysis on different multimodal features to identify the behavioral patterns used by politicians when it comes to deception.

Multimodal Political Deception Detection

The wide range of usage and application of wearable sensors like as smart watches provide access to precious inertial sensor data that is usable in human identification based on their gait pattern. A large number of studies have been conduced on extracting high-level and various heuristic features out of inertial sensor data to identify discriminative gait signatures and distinguish the target individual from others. However, complexity of the collected data from inertial sensors, detachment between the predictive learning models and intuitive feature extraction module increase the error rate of manual feature extraction. We propose a new method for the task of human gait identification based on spectro-temporal two dimensional expansion of gait cycle. Then, we design a deep convolutional neural network learning in order to extract discriminative features from the two dimensional expanded gait cycles and also jointly optimize the identification model simultaneously. We propose a systematic approach for processing nonstationary motion signals with the application of human gait identification with 3 main elements: first gait cycle extraction, second spectro-temporal representation of gait cycle, and third deep convolutional learning. We collect motion signal from 5 inertial sensors placed at different locations including lower-back, chest, right knee, right ankle, and right hand wrist. We pre-process the acquired raw signals by motion signal processing and then we propose an efficient heuristic segmentation methodology and extract gait cycle from the segmented and processed data. Spectro-temporal two dimensional features are extracted by merging key instantaneous temporal and spectral descriptors in a gait cycle which is capable of characterizing the non-stationarities in each gait cycle inertial data. The two dimensional time-frequency distribution representation of the gait cycle extracted from acquired inertial sensor data from 10 subjects are fed as input to the designed and proposed 10 layers DCNN architecture. Based on our experimental analysis, 93.36% accuracy was achieved for subject identification task.

Priyanka Asnani

Papers

ECG-based driver inattention identification during naturalistic driving using Mel-frequency cepstrum 2-D transform and convolutional neural networks

Multimodal Political Deception Detection

Motion-Based Gait Identification Using Spectro-temporal Transform and Convolutional Neural Networks