On the Sparsest Representation of Electrocardiograms

In recent years, telecardiology has been growing in significance, due to the shortage of local caregivers in various parts of the world. As the cardiac data volume grows, compact representation becomes imperative in view of bandwidth, storage, power and other constraints. In this backdrop, we present empirical studies on electrocardiogram (ECG) signal representation using a wide variety of wavelet bases. Specifically, we arrange the transform coefficients in decreasing order of magnitude, and count the number of coefficients accounting for 99% of the signal energy (a sparser representation requires less number). We observe that 'Symlet' and 'Daubechies' families generally offer more compact representation compared to Meyer wavelet as well as biorthogonal and reverse biorthogonal families. In particular, the sparsest representation is provided by the 'sym4' (closely followed by the 'db4') wavelet basis for a broad class of ECG signals. Interestingly, this behavior is observed quite consistently across all fifteen (twelve standard and three Frank) leads. Our study assumes significance in the context of basis selection for various ECG signal processing applications, including compression, denoising and compressive sensin

On the Sparsest Representation of Electrocardiograms

In recent years, telecardiology has been growing in significance, due to the shortage of local caregivers in various parts of the world. As the cardiac data volume grows, compact representation becomes imperative in view of bandwidth, storage, power and other constraints. In this backdrop, we present empirical studies on electrocardiogram (ECG) signal representation using a wide variety of wavelet bases. Specifically, we arrange the transform coefficients in decreasing order of magnitude, and count the number of coefficients accounting for 99% of the signal energy (a sparser representation requires less number). We observe that 'Symlet' and 'Daubechies' families generally offer more compact representation compared to Meyer wavelet as well as biorthogonal and reverse biorthogonal families. In particular, the sparsest representation is provided by the 'sym4' (closely followed by the 'db4') wavelet basis for a broad class of ECG signals. Interestingly, this behavior is observed quite consistently across all fifteen (twelve standard and three Frank) leads. Our study assumes significance in the context of basis selection for various ECG signal processing applications, including compression, denoising and compressive sensin

Wavelets and Imaging Informatics: A Review of the Literature

AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics

Program latihan industri di Kolej Universiti Teknologi Tun Hussein Onn : kajian terhadap perlaksanaan sistem penilaian

Kajian yang dijalankan adalah bertajuk "Program Lalilian lndustri Di Kolej Universiti Teknologi Tun Hussein Onn : Kajian Terhadap Perlaksanaan Sistem Penilaian". Sampel terdin daripada 6 orang pakar serta 63 orang pelajar yang terlibat dalam latihan industri. Maklumat yang diperolehi berdasarkan kaedah kualitatif dan kuantitatif Data dianalisis untuk meninjau kaedah penilaian yang dijalankan dan seterusnya memastikan apakali sistem penilaian yang perlu diperbaiki. Secara keseluruhannya, kebanyakan responden berpendapat bahawa sistem penilaian yang sedia ada adalah perlu diperbaki dan disistematikkan selaras dengan ISO 9000 : 2001. Berdasarkan daripada keputusan yang diperolehi dan bimbingnan pakar dari Unit Latihan lndustri KUiTTHO, maka satu "Buku Panduan Penilaian Latihan lndustri" dihasilkan dengan panduan yang ringkas dan lampiran borang-borang yang telah diperbaiki dan diubahsuai. Diharapkan produk mi dapat digunakan untuk masa-masa akan datang

Reduce The Noise in Speech Signals Using Wavelet Filtering

 تنخفض قدرة قنوات البيانات غالبا ما بسبب الضوضاء وتشوه الإشارات المرسلة . يستخدم تخفيض الضوضاء في مجالات  مختلفة (حيث لا يمكن عزل الإشارات المرسلة من الضوضاء والتشويه): في التعرف على الكلام ومعالجة الصور وأنظمة الاتصالات المتنقلة ومعالجة الإشارات الطبية والأنظمة الراديوية والرادارية وما إلى ذلك. توضح هذه الورقة مشكلة وجود ضوضاء في إشارات الكلام. ويتم النظر في نموذج ضوضاء غوسية بيضاء مضافة وإضافته إلى إشارة الكلام - نمذجة عملية الضوضاء. حيث تم   دراسة الميزات الاساسية للمويجات المستخدمة لتقليل الضوضاء. وتم النظر في الخوارزمية الاساسية لعميلة  ازالة الضوضاء باستخدام تقنيات تحليل المويجات. تم بناء التنفيذ العملي للحد من الضوضاء. تم رسم الاشارة الاصلية والاشاره المشوهة  والاشاره المستخلصه بعد تقليل الضوضاء. تم تحليل نتائج إلغاء الضوضاء باستخدام أسر مختلفة من المويجات، حيث تم رسم الاشكال للصلات المتبادلة بين إشارات الكلام المشوهة والنظيفة.تقليل الضوضاء نفذ باستخدام برنامج ماتلاب.The capacity of the data channels is often reduced due to noise and distortion of the transmitted signals. Noise reduction is used in various areas (where from noise and distortion the transmitted signals cannot be isolated): speech / speaker recognition, image processing, mobile communication systems, medical signal processing, radio and radar systems, etc. This paper illustrates the problem of the presence of noise in speech signals. A model of additive white Gaussian noise is considered and adding it to the speech signal – modeling of noise process. The main features of wavelets, which used in noise reduction, are described. The main algorithm of the noise cancellation process using wavelet analysis techniques is considered. Carried out the practical implementation of noise reduction. The graphs of the original, noisy and cleaned signals are plotted. An analysis of the results of noise cancellation was carried out using different families of wavelets, graphs of the cross correlation of noisy and clean speech signals are plotted. Noise reduction carried out using Matlab programing

Study on high Performance and Effective Watermarking Scheme using Hybrid Transform (DCT-DWT)

Nowadays healthcare infrastructure depends on Hospital Information Systems (HIS), Radiology Information Systems (RIS),Picture archiving and Communication Systems (PACS) as these provide new ways to store, access and distribute medical data . It eliminates the security risk. Conversely, these developments have introduced new risks for unsuitable deployment of medical information flowing in open networks, provided the effortlessness with which digital content can be manipulated. It is renowned that the integrity and confidentiality of medical data is a serious topic for ethical and legal reasons. Medical images need to be kept intact in any condition and prior to any operation as well need to be checked for integrity and verification. Watermarking is a budding technology that is capable of assisting this aim. In recent times, frequency domain watermarking algorithms have gained immense importance due to their widespread use. Subsequently, the watermark embedding and extraction are performed in frequency domain using the presented scheme. The proposed watermarking scheme, the watermark extraction compared with the original image for calculating SSIM.The effectiveness of the proposed watermarking scheme is demonstrated with the aid of experimental results

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Offline and real time noise reduction in speech signals using the discrete wavelet packet decomposition

This thesis describes the development of an offline and real time wavelet based speech enhancement system to process speech corrupted with various amounts of white Gaussian noise and other different noise types

High-resolution multipath channel parameter estimation using wavelet analysis

This thesis explores the novel use of wavelet analysis as a high-resolution digital signal processing algorithm for multipath channel parameter estimation. The results obtained from this research indicate that this wavelet-based digital signal processing algorithm overcomes the resolution limitation in conventional high-resolution algorithm. This may provide a more cost-effective means of implementing channel sounding equipments for very high-resolution measurements