Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, F 1 -score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.
The central aim of this paper is to provide a Com pressed Sensing (CS) approach to implement a multifrequency signaling system so as to address its specific real time constraints and challenges. Here, the implementation is illustrated by using real time Dual Tone Multi-Frequency (DTMF) signaling system. DTMF is a signaling scheme that is developed by utilizing voice frequency tones. In this paper, an attempt is made to apply the theory of CS to a real time DTMF signaling system. Here the CS methodology is applied to the source model that generates DTMF signals. To work with practical systems, modifications are made to the CS framework by adding noise to the measurements. This paper proposes a simple and effective sparse signal recovery algorithm that uses linear algebra theory to convert an underde termined system to an overdetermined system. The methodology extends to a variety of other situations and higher dimensions.
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