The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

/pdf/an-iot-based-computational-framework-for-healthcare-480zcfp6qs.pdf

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

/pdf/automatic-qrs-complex-detection-using-two-level-2op06tzma8.pdf

Automatic QRS complex detection using two-level convolutional neural network

Medical & biological engineering & computing

Grammars are widely used to describe string languages such as programming and natural languages and, more recently, biosequences. Moreover, since the 1980s grammars have been used in computer vision and related areas. Some factors accountable for this increasing use regard its relatively simple understanding and its ability to represent some semantic pattern models found in images, both spatially and temporally. The objective of this article is to present an overview regarding the use of syntactic pattern recognition methods in image representations in several applications. To achieve this purpose, we used a systematic review process to investigate the main digital libraries in the area and to document the phases of the study in order to allow the auditing and further investigation. The results indicated that in some of the studies retrieved, manually created grammars were used to comply with a particular purpose. Other studies performed a learning process of the grammatical rules. In addition, this article also points out still unexplored research opportunities in the literature.

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Pedro%20-%20Using%20Grammars%20for%20Pattern%20Recognition%20in%20Images%20-%202013.pdf

Using grammars for pattern recognition in images: A systematic review

Dynamic thresholding based efficient QRS complex detection with low computational overhead

The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration. The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks’ amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed. Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

/pdf/real-time-qrs-complex-detection-using-dfa-and-regular-3tsbhf0ijr.pdf

Real time QRS complex detection using DFA and regular grammar

A robust QRS complex detection using regular grammar and deterministic automata

Image segmentation is an important task in the image processing and represents a very active research field such as on medical image processing. The aim of this work is to segment an image and make an automated area estimation based on grammar. The entity “language” will be projected to the entity “image” to perform structural analysis and parsing of the image. We achieve to define an image as a set of words based on an alphabet. An object is assimilated as a word recognized by an automaton. We will show how the idea of grammar-based segmentation is applied to synthetic and real problems of cardio-graphic image processing.

Grammar-based image segmentation and automatic area estimation

This paper shows that the grammar formalism can be pushed to be applied for the description and the classification of electrocardiograms signals (ECG). We will describe an ECG signal as a sequence of tokens based on specific vocabularies and a set of grammatical rules. QRS complexes, RR distances, PR and QT intervals will be calculated. This type of work is intended for medical diagnosis assistance of an ECG signal.

/pdf/grammar-formalism-for-ecg-signal-interpretation-and-1by7typoqd.pdf

Grammar Formalism for ECG Signal Interpretation and Classification

Grammar and the theory of language have been around for a long time. So far, grammar formalism has been used for programming and formal languages to describe natural languages and software engineering. However, the grammatical discipline can be extended to join other areas of applications such as ECG signal analysis. Thus, this chapter will provide an overview of the related methods and show that syntactic methods and regular grammar can be supported for ECG analysis and especially for the recognition of QRS complexes. A comparative study with a huge number of known methods will be presented.

Salah Hamdi

Papers

Real time QRS complex detection using DFA and regular grammar

A robust QRS complex detection using regular grammar and deterministic automata

Grammar-based image segmentation and automatic area estimation

Grammar Formalism for ECG Signal Interpretation and Classification

Syntactic Methods for ECG Signal Diagnosis and QRS Complexes Recognition