In this paper, we present a methodology for classifying normal, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy recordings by analysing single lead ECG signal of short duration. In a two layer binary cascaded approach proposed in our methodology, an unlabelled recording is initially classified into one of the two intermediate classes ('normal+others' and 'AF+noisy') at the first layer before actual classification at the second layer. The Physionet Challenge 2017 dataset containing more than 8500 ECG recordings are used for creation of training models and interval validation. The proposed methodology yields an average F1-score of 0.91, 0.79 and 0.77 respectively in classifying normal, AF and other rhythms on the training dataset using 5-fold cross validation. Results also show that, the said methodology, when applied on a hidden test set maintained by the challenge organisers yields F1-score values of 0.92, 0.86 and 0.74 in classifying the same. IntroductionAtrial Fibrillation (AF) is a common type of heart disease that leads to stroke, heart failure or other complications. Millions of people get affected by AF every year and the prevalence of the disease is likely to increase. Noninvasive detection of AF is a popular area of research for quite a long time. Irregularities in heart beat is considered to be the most common symptom of AF and can be traced in an ECG. However, being an episodic event an accurate detection of AF is not always trivial. Conventional AF detectors ([1], [2], [3]) are mostly of atrial activity analysisbased or ventricular response analysis-based methods. The absence of P waves or the presence of f waves in the TQ interval are searched in atrial activity analysis-based AF detectors. On the other hand, time, frequency and morphological features are extracted from RR intervals to identity the heart beat irregularity in ventricular response analysisbased methods.However, the prior art methods have certain limitations regarding real time deployments. 1) Most of them are validated on clinically accepted 12 lead ECG signals, recorded for a relatively longer duration. 2) Algorithms are mostly applied on carefully selected clean data. However, in practical scenario, ECG signals are often noisy in nature. 3) Size of the test dataset are often not adequate for making a conclusion. 4) Most prior arts perform binary classification between AF and normal recordings only. However, there are many non-AF abnormal rhythms (like tachycardia, bradycardia, arrhythmia etc) which exhibits heart beat pattern similar to AF. Considering them in the in the dataset makes the classification task more challenging. In this paper we propose a robust algorithm for classifying normal, AF, other abnormal rhythms and noisy ECG recordings. The diverse ECG dataset, provided in Physionet challenge 2017 [4] is used for internal performance evaluation and creating the training models. Information regarding individual recordings are not available regarding the other rhythms in the dataset as...

Semiconducting metal oxide-based gas sensors have inadequate selectivity as they are responsive toward a variety of gases. Here, we report the implementation of gas sensing kinetic analysis of the sensor to identify the tested volatile organic compounds (VOCs) (2-propanol, formaldehyde, methanol, and toluene) precisely. A single chemiresistive sensor was employed having tin oxide-based hollow spheres as the sensing material, which were obtained by chemical synthesis. The gas sensing measurements were conducted in a dynamic manner where the sensor displayed excellent response with high sensitivity. Eley–Rideal model was adopted to obtain the kinetic properties of the gas sensing phenomenon through theoretical fitting of response transient curves and their corresponding kinetic parameters. The calculated characteristic kinetic properties were further examined to discriminate among different VOCs. The approach of using gas sensing kinetic analysis for multiple gas discrimination is an attractive solution to mitigate the problem of cross-sensitivity for resistive gas sensors.

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Privacy breaching attacks pose considerable challenges in the development and deployment of Internet of Things (IoT) applications. Though privacy preserving data mining (PPDM) minimizes sensitive data disclosure probability, sensitive content analysis, privacy measurement and user's privacy awareness issues are yet to be addressed. In this paper, we propose a privacy management scheme that enables the user to estimate the risk of sharing private data like smart meter data. Our focus is to develop robust sensitivity detection, analysis and privacy content quantification scheme from statistical disclosure control aspect and information theoretic model. We depict performance results using real sensor data.

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