Diagnosis of Critical Diseases for Cardiac Patients using ECG Signal

 Results Model hyperparameters Hyper-parameters Value Gradient Decay Factor 0.9000 Squared Gradient Decay Factor 0.9990 Epsilon 1.0000e-08 Initial Learn Rate 0.0020 Learn Rate Schedule 'none' Learn Rate Drop Factor 0.1000 Learn Rate Drop Period 10 Hidden Layers 45 L2Regularization 1.0000e-04 Gradient Threshold Method 'l2norm' Max Epochs 800 Mini Batch size 1800 Sequence Length 'shortest' Sequence Padding Value 0 Sequence Padding Direction 'right' Batch Normalization Statistics 'population' Confusion Matrix for WST features the confusion chart shown abo ve rep

The selected features are the following: mean, max value, root mean square, square root mean, standard deviation, variance, shape factor (with RMS), shape factor (with SRM), crest factor, latitude factor, impulse factor, skewness, kurtosis, normalized 5th central moment, normalized 6th central moment Mathematical Expressions These features are used on each observation of the ECG dataset. Once the features have been extracted. It is then fed into the LSTM network  

  What Is an Arrhythmia? An arrhythmia, or irregular heartbeat, is a problem with the rate or rhythm of your heartbeat. Your heart may beat too quickly, too slowly, or with an irregular rhythm. It is normal for your heart rate to speed up during physical activity and to slow down while resting or sleeping. It is also normal to feel as if your heart skips a beat occasionally. But a frequent irregular rhythm may mean that your heart is not pumping enough blood to your body. You may feel dizzy, faint, or have other symptoms.  Arrhythmias are treatable with medicine or procedures to control the irregular rhythms. If not treated, arrhythmias can damage the heart, brain, or other organs. This can lead to life-threatening stroke, heart failure, or cardiac arrest. During cardiac arrest, the heart suddenly and unexpectedly stops beating, causing death if it is not treated within minutes. If you have been diagnosed with an arrhythmia, your doctor may talk to you about healthy lifestyle changes y

  Fourier Vs Wavelet Transform What Is Fourier Transform? An idea you’ll find all over science, math and engineering is the Fourier Transform (FT) . The FT decomposes a function into simple sines and cosines (i.e, waves). In theory, any function can be represented in this way — that is, as a sum of (possibly infinite) sine and cosine functions of different amplitudes and frequencies. I’ve provided a toy example below. Here, I’ve translated the top signal from the time domain to the frequency domain. In other words, we change the x-axis from time to frequency. The way to interpret peaks in the bottom plot is that the original signal represents the sum of two simple sine waves with frequencies one and two Hz, respectively. What Is a Wavelet Transform A major disadvantage of the Fourier transform is it captures global frequency information, meaning frequencies that persist over an entire signal. This kind of signal decomposition may not serve all applications well, for example electrocard

Objective This Project Utilizes single lead ECG data acquired from the physio net database. Feature Extraction Techniques used in this project include wavelet scattering transform and 15 statistical time feature. A neural and non-neural based approach has been used to create a working classifier Let me show you a visual representation Link to Access the dataset: https://physionet.org/about/database/ Before the Next Post let me leave you with a visualization of wavelet scattering Till the Next ONE