M. R. Homaeinezhad, A. Ghaffari, H. Najjaran Toosi, M. Tahmasebi, M. M. Daevaeiha,
Volume 7, Issue 1 (3-2011)
Abstract
In this study, a new long-duration holter electrocardiogram (ECG) major events detection-delineation algorithm is described which operates based on the false-alarm error bounded segmentation of a decision statistic with simple mathematical origin. To meet this end, first three-lead holter data is pre-processed by implementation of an appropriate bandpass finite-duration impulse response (FIR) filter and also by calculation of the Euclidean norm between corresponding samples of three leads. Then, a trous discrete wavelet transform (DWT) is applied to the resulted norm and an unscented synthetic measure is calculated between some obtained dyadic scales to magnify the effects of low-power waves such as P or T-waves during occurrence of arrhythmia(s). Afterwards, a uniform length window is slid sample to sample on the synthetic scale and in each slid, six features namely as summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second order differentiation, curve length, area and variance of the excerpted segment are calculated. Then all feature trends are normalized and superimposed to yield the newly defined multiple-order derivative wavelet based measure (MDWM) for the detection and delineation of ECG events. In the next step, a α-level Neyman-Pearson classifier (which is a false-alarm probability-FAP controlled tester) is implemented to detect and delineate QRS complexes. To show advantages of the presented method, it is applied to MIT-BIH Arrhythmia Database, QT Database, and T-Wave Alternans Database and as a result, the average values of sensitivity and positive predictivity Se = 99.96% and P+ = 99.96% are obtained for the detection of QRS complexes, with the average maximum delineation error of 5.7 msec, 3.8 msec and 6.1 msec for P-wave, QRS complex and T-wave, respectively showing marginal improvement of detection-delineation performance. In the next step, the proposed method is applied to DAY hospital high resolution holter data (more than 1,500,000 beats including Bundle Branch Blocks-BBB, Premature Ventricular Complex-PVC and Premature Atrial Complex-PAC) and average values of Se=99.98% and P+=99.97% are obtained for QRS detection. In summary, marginal performance improvement of ECG events detection-delineation process in a widespread values of signal to noise ratio (SNR), reliable robustness against strong noise, artifacts and probable severe arrhythmia(s) of high resolution holter data and the processing speed 163,000 samples/sec can be mentioned as important merits and capabilities of the proposed algorithm.
Kausar Ahmed, Bibhor Regan Gomes, S.m. Jobair Hossain, Emran Khan Musa, Fuyad Hasan Bhoyan, Md Humaion Kabir Mehedi, Jia Uddin,
Volume 22, Issue 3 (9-2026)
Abstract
Fires in indoor spaces such as residential and office buildings pose significant threats to human lives and property, causing substantial damage each year. Early and accurate fire detection plays a critical role in mitigating these risks and ensuring timely responses. However, conventional methods such as smoke sensors, temperature indicators, and standalone computer vision models suffer from limitations like false alarms, delayed detection, and high hardware demands. To address these challenges, we propose a novel three-layer verification framework for indoor fire detection to reduce false alarms, integrating smoke sensors, computer vision, and temperature monitoring into a multi-modal validation framework. The process begins with smoke sensors detecting potential fire incidents. The custom-trained YOLOv11n computer vision model verifies the detection using predefined thresholds, allowing immediate response without waiting for temperature escalation. If the computer vision model does not confirm the fire, the system initiates a temperature check as a final validation layer. Experimental evaluation of our model demonstrates a significantly high precision of 0.979 and a recall of 0.971. This layered approach ensures comprehensive detection, balancing reliability and resource efficiency. Our proposed hybrid AI-physical systematic framework demonstrates significant potential in reducing false alarms, improving detection accuracy, and prioritizing methodological scalability over industrial hardware. It lays the foundation for more reliable and energy-efficient fire safety solutions in smart buildings and industrial safety applications.
