Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

HEART MONITORING VIA WIRELESS ECG

The monitoring of heart had been a complex task. Acquiring ECG of the chronic patient spending most of their time outside the hospital had been a trivial task. Recording of ECG of such patients using wireless method is further challenging. This paper presents various methods of wireless ECG acquisition, their limitations and challenges. Cardiomobile, Flexible wireless ECG are the examples of such systems that are available in the medical world for wireless ECG

Source Code Verification for Embedded Systems using Prolog

System relevant embedded software needs to be reliable and, therefore, well tested, especially for aerospace systems. A common technique to verify programs is the analysis of their abstract syntax tree (AST). Tree structures can be elegantly analyzed with the logic programming language Prolog. Moreover, Prolog offers further advantages for a thorough analysis: On the one hand, it natively provides versatile options to efficiently process tree or graph data structures. On the other hand, Prolog's non-determinism and backtracking eases tests of different variations of the program flow without big effort. A rule-based approach with Prolog allows to characterize the verification goals in a concise and declarative way. In this paper, we describe our approach to verify the source code of a flash file system with the help of Prolog. The flash file system is written in C++ and has been developed particularly for the use in satellites. We transform a given abstract syntax tree of C++ source code into Prolog facts and derive the call graph and the execution sequence (tree), which then are further tested against verification goals. The different program flow branching due to control structures is derived by backtracking as subtrees of the full execution sequence. Finally, these subtrees are verified in Prolog. We illustrate our approach with a case study, where we search for incorrect applications of semaphores in embedded software using the real-time operating system RODOS. We rely on computation tree logic (CTL) and have designed an embedded domain specific language (DSL) in Prolog to express the verification goals.Comment: In Proceedings WLP'15/'16/WFLP'16, arXiv:1701.0014

Prerequisites for Affective Signal Processing (ASP)

Although emotions are embraced by science, their recognition has not reached a satisfying level. Through a concise overview of affect, its signals, features, and classification methods, we provide understanding for the problems encountered. Next, we identify the prerequisites for successful Affective Signal Processing: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal processing community. Using these directives, a critical analysis of a real-world case is provided. This illustrates that the prerequisites can become a valuable guide for Affective Signal Processing (ASP)

SSTS: A syntactic tool for pattern search on time series

We would like to acknowledge the financial support obtained from North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM), NORTE-01-0145-FEDER-000026. We would like to acknowledge as well the projects AHA CMUP-ERI/HCI/0046 and INSIDE CMUP-ERI/HCI/051/2013 both financed by Fundcao para a Ciencia e Tecnologia (FCT).Nowadays, data scientists are capable of manipulating and extracting complex information from time series data, given the current diversity of tools at their disposal. However, the plethora of tools that target data exploration and pattern search may require an extensive amount of time to develop methods that correspond to the data scientist's reasoning, in order to solve their queries. The development of new methods, tightly related with the reasoning and visual analysis of time series data, is of great relevance to improving complexity and productivity of pattern and query search tasks. In this work, we propose a novel tool, capable of exploring time series data for pattern and query search tasks in a set of 3 symbolic steps: Pre-Processing, Symbolic Connotation and Search. The framework is called SSTS (Symbolic Search in Time Series) and uses regular expression queries to search the desired patterns in a symbolic representation of the signal. By adopting a set of symbolic methods, this approach has the purpose of increasing the expressiveness in solving standard pattern and query tasks, enabling the creation of queries more closely related to the reasoning and visual analysis of the signal. We demonstrate the tool's effectiveness by presenting 9 examples with several types of queries on time series. The SSTS queries were compared with standard code developed in Python, in terms of cognitive effort, vocabulary required, code length, volume, interpretation and difficulty metrics based on the Halstead complexity measures. The results demonstrate that this methodology is a valid approach and delivers a new abstraction layer on data analysis of time series.publishersversionpublishe

Bottom-up design of artificial neural network for single-lead electrocardiogram beat and rhythm classification

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined in terms of perfecting individual sub-systems to improve the over all system performance is used. Sub-systems include pre-processing, QRS detection and fiducial point estimations, feature calculations, and pattern classification. Inaccuracies in time-domain fiducial point estimations are overcome with the derivation of features in the frequency domain. Feature extraction in frequency domain is based on a spectral estimation technique (combination of simulation and subtraction of a normal beat). Auto-regressive spectral estimation methods yield a highly sensitive spectrum, providing several local features with information on beat classes like flutter, fibrillation, and noise. A total of 27 features, including 16 in time domain and 11 in frequency domain are calculated. The entire data and problem are divided into four major groups, each group with inter-related beat classes. Classification of each group into related sub-classes is performed using smaller feed-forward neural networks. Input feature sub-set and the structure of each network are optimized using an iterative process. Optimal implementations of feed-forward neural networks provide high accuracy in beat classification. Associated neural networks are used for the more deterministic rhythm-classification task. An accuracy of more than 85% is achieved for all 13 classes included in this study. The system shows a graceful degradation in performance with increasing noise, as a result of the noise consideration in the design of every sub-system. Results indicate a neural network-based bottom-up design of single-lead ECG classification is able to provide very high accuracy, even in the presence of noise, flutter, and fibrillation

Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction

We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art