A Comparative Performance of Discrete Wavelet Transform Implementations Using Multiplierless

Background Early detection of outbreaks of transmissible diseases is essential for public health. This study aimed to determine the performance of the wavelet‐based outbreak detection method (WOD) in detecting outbreaks and to compare its performance with the Poisson regression‐based model and exponentially weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran. Method The data on suspected cases of pertussis from 25th February 2012 to 23rd March 2018 in Iran was used. The performance of the WOD (Daubechies 10 [db10] and Haar wavelets), Poisson regression‐based method, and EWMA Compared in terms of timeliness and detection of outbreak days using the simulation of different outbreaks. In the current study, two simulations were used, one based on retrospectively collected data (literature‐based) on pertussis cases and another one on a synthetic dataset created by the researchers. The sensitivity, specificity, false alarm, and false‐negative rate, positive and negative likelihood ratios, under receiver operating characteristics areas, and median timeliness were used to assess the performance of the methods. Results In a literature‐based outbreak simulation, the highest and lowest sensitivity, false negative in the detection of injected outbreaks were seen in db10, with sensitivity 0.59 (0.56‐0.62), and Haar wavelets with 0.57 (0.54‐0.60). In the researcher simulated data, the EWMA (K = 0.5) with sensitivity 0.92 (0.90‐0.94) had the best performance. About timeliness, the WOD methods showed the best performance in the early warning of the outbreak in both simulation approaches. Conclusion Performance of the WOD in the early alarming outbreaks was appropriate. However, this method would be best used along with other methods of public health surveillance.

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“…The DWT has different wavelets. More details about the type of wavelets are described in other sources 17,30‐32 …”

Section: Methodsmentioning

confidence: 99%

The comparative performance of wavelet‐based outbreak detector, exponential weighted moving average, and Poisson regression‐based methods in detection of pertussis outbreaks in Iranian infants: A simulation‐based study

Alimohamadi

Zahraei

Karami

et al. 2020

Pediatric Pulmonology

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“…The DWT has different wavelets. More details about the type of wavelets are described in other sources (14,(25)(26)(27).…”

Section: Methods Of Outbreak Detectionmentioning

confidence: 99%

Timely detection of Pertussis outbreaks in Iran: The comparison performance of Wavelet-based outbreak detector, Exponential weighted moving average, and Poisson regression-based methods

Alimohamadi¹,

Zahraei²,

Karami³

et al. 2020

Preprint

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Background Early detection of outbreaks is very important for surveillance systems. Due to the importance of the subject and lack of similar studies in Iran, the aim of this study was to determine the performance of the Wavelet-Based Outbreak detection method)WOD(in detecting outbreaks and to compare its performance with Poisson regression-based model and Exponential weighted moving average (EWMA) using data of simulated pertussis outbreaks in Iran. Methods The data on suspected cases of pertussis from 25th February 2012 to 23rd March 2018 in Iran was used. The performance of the WOD (Daubechies 10 and Haar wavelets), Poisson regression-based method, and EWMA Compared in terms of timeliness and detection of outbreak days using the simulation of different outbreaks (literature-based and researcher-made outbreaks). The sensitivity, specificity, false alarm and false negative rate, positive and negative likelihood ratios, under ROC areas and median timeliness were used to assess the performance of the methods. Results In a literature-based outbreak simulation, the highest and lowest sensitivity, false negative in the detection of injected outbreaks were seen in Daubechies 10 (db10), with sensitivity 0.59 (0.56-0.62), and Haar wavelets with 0.57 (0.54-0.60). In the researcher-made outbreaks, the EWMA (K=0.5) with sensitivity 0.92 (0.90-0.94) had the best performance. About timeliness, the WOD methods showed the best performance in the early warning of the outbreak in both simulation approaches. Conclusions Performance of the WOD in the early alarming outbreaks was appropriate. However, it's better as the method was used along with other methods in public health surveillance systems.

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“…Key disadvantages identified include shift sensitivity, where DWT's output can vary significantly with slight input shifts, limiting its use in precise signal localization; poor directionality, which restricts its effectiveness in multidimensional signal processing, like image analysis; and the inability to preserve phase information, crucial for detailed signal structure and timing (Fernandes et al, 2004). Furthermore, the computational complexity and resource consumption of conventional DWT, as discussed by Alzaq et al (Alzaq and Üstündag, 2018) present further challenges, particularly in areas requiring lowfrequency focus.…”

mentioning

confidence: 99%

DWSTr: a hybrid framework for ship-radiated noise recognition

Wang,

Zhang,

Huang

et al. 2024

Front. Mar. Sci.

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The critical nature of passive ship-radiated noise recognition for military and economic security is well-established, yet its advancement faces significant obstacles due to the complex marine environment. The challenges include natural sound interference and signal distortion, complicating the extraction of key acoustic features and ship type identification. Addressing these issues, this study introduces DWSTr, a novel method combining a depthwise separable convolutional neural network with a Transformer architecture. This approach effectively isolates local acoustic features and captures global dependencies, enhancing robustness against environmental interferences and signal variability. Validated by experimental results on the ShipsEar dataset, DWSTr demonstrated a notable 96.5\% recognition accuracy, underscoring its efficacy in accurate ship classification amidst challenging conditions. The integration of these advanced neural architectures not only surmounts existing barriers in noise recognition but also offers computational efficiency for real-time analysis, marking a significant advancement in passive acoustic monitoring and its application in strategic and economic contexts.

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A Comparative Performance of Discrete Wavelet Transform Implementations Using Multiplierless

Cited by 8 publications

References 37 publications

The comparative performance of wavelet‐based outbreak detector, exponential weighted moving average, and Poisson regression‐based methods in detection of pertussis outbreaks in Iranian infants: A simulation‐based study

The comparative performance of wavelet‐based outbreak detector, exponential weighted moving average, and Poisson regression‐based methods in detection of pertussis outbreaks in Iranian infants: A simulation‐based study

Timely detection of Pertussis outbreaks in Iran: The comparison performance of Wavelet-based outbreak detector, Exponential weighted moving average, and Poisson regression-based methods

DWSTr: a hybrid framework for ship-radiated noise recognition

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