Design of moving average chart and auxiliary information based chart using extended EWMA

Naveed, Muhammad; Azam, Muhammad; Nawaz, Muhammad Shujaat; Aslam, Muhammad; Saeed, Muhammad

doi:10.1038/s41598-023-32781-4

Cited by 5 publications

(2 citation statements)

References 47 publications

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“…The concept of Moving Average (MA) was initially introduced in 1965 and has since found widespread application across diverse domains for analyzing time series data 18 . For instance, in trading, a common practice involves employing two moving averages -a shorter-term moving average and a longer-term moving average -in tandem to identify trading events.…”

Section: Moving Averagementioning

confidence: 99%

ECG Heartbeat classification using Progressive Moving Average Transform

Mokhtari,

Belhaouari,

Kassoul

et al. 2024

Preprint

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This paper presents the Progressive Moving Average Transform (PMAT), a novel signal transformation method for converting time-domain signals into 2D representations by progressively computing Moving Averages (MAs) with varying window sizes. The approach aims to enhance signal analysis and classification, particularly in the context of heartbeat classification. Our approach integrates PMAT with a 2D-Convolutional Neural Network (CNN) model for the classification of ECG heartbeat signals. The 2D-CNN model is employed to extract meaningful features from the transformed 2D representations and classify them efficiently. To assess the effectiveness of our approach, we conducted extensive simulations utilizing two widely-used databases: the MIT-BIH database and the INCART database, chosen to cover a wide range of heartbeats. Our experiments involved classifying more than 6 heartbeat types grouped into three main classes. Results indicate high accuracy and F1-scores, with 99.09% accuracy and 92.13% F1-score for MIT-BIH, and 98.37% accuracy and 79.37% F1-score for INCART. Notably, the method demonstrates robustness when trained on one database and tested on another, achieving accuracy rates exceeding 95% in both cases. These findings underscore the effectiveness and stability of the proposed approach in accurately classifying heartbeats across different datasets, suggesting its potential for practical implementation in medical diagnostics and healthcare systems.

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Section: Moving Averagementioning

confidence: 99%

ECG Heartbeat classification using Progressive Moving Average Transform

Mokhtari,

Belhaouari,

Kassoul

et al. 2024

Preprint

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“…The approach of gauging efficiency solely by increasing the sample size is deemed flawed. In essence, the work by Saleh et al 53 presents a comparison that appears illogical, as it overlooks the assumptions inherent to repetitive sampling and seems to disregard the optimization methods outlined in existing literature. Upon reviewing the study by Saleh et al 53 , it becomes evident that their primary intention was to criticize the applicability of repetitive sampling in future scenarios, potentially undermining its practical use.…”

Section: Introductionmentioning

confidence: 99%

Efficient signed-rank based EWMA and HWMA repetitive control charts for monitoring process mean with and without auxiliary information

Shafqat,

Zhensheng,

Aslam

2023

Sci Rep

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Control charts are powerful tools to observe the presentation of the manufacturing process. Mostly, when the data in industries come from the process may follow non-normal or unknown distributions. So, the distribution-free control charts are useful in practice when the possibility model of the process productivity is unknown. In such situations, the correct selection of the sampling mechanism is beneficial for process examination. This paper proposes a nonparametric exponentially weighted moving average signed-rank (EWMA-SR) and also proposed a homogeneously weighted moving average Signed-Rank (HWMA-SR) control charts for examining the small shift in process with the help of an auxiliary variable (in the form of a regression estimator) by using an efficient plan, namely, a repetitive sampling plan. The proposal’s presentation is evaluated and matched with its complements for different symmetric distributions by using some famous run length properties including average run length, median run length, and standard deviation of run length.

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