From 81567490e73df388980ac9a60bccba4c65528dd4 Mon Sep 17 00:00:00 2001 From: RichieHakim Date: Wed, 7 Feb 2024 18:32:48 -0500 Subject: [PATCH] imrpove ecg example --- README.md | 57 +++++++++++++++++++++---------------- docs/media/example_ECG.png | Bin 78043 -> 126570 bytes 2 files changed, 33 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 87bc409..40c7cd2 100644 --- a/README.md +++ b/README.md @@ -10,20 +10,20 @@ This is a novel python implementation of the variable Q-transform that was developed due to the need for a more accurate and flexible VQT for the use in research. It is battle-tested and has been used in a number of research projects.
-- Accuracy: The approach is different in that it is a **direct implementation** +- **Accuracy**: The approach is different in that it is a **direct implementation** of a spectrogram via a Hilbert transformation at each desired frequency. This results in an exact computation of the spectrogram and is appropriate for research applications where accuracy is critical. The implementation seen in `librosa` and `nnAudio` uses recursive downsampling, which can introduce artifacts in the spectrogram under certain conditions. -- Flexibility: The parameters and codebase are less complex than in other +- **Flexibility**: The parameters and codebase are less complex than in other libraries, and the filter bank is fully customizable and exposed to the user. Built in plotting of the filter bank makes tuning the parameters easy and intuitive. -- Speed: The backend is written using PyTorch, and allows for GPU acceleration. -In most cases it is faster than the `librosa` implementation, especially when -using a GPU. Also, when the `downsample`/`hop_length` parameter is low (<32), it -is as fast or faster than the `nnAudio` implementation. +- **Speed**: The backend is written using PyTorch, and allows for GPU acceleration. +It is faster than the `librosa` implementation, and roughly as fast as the +`nnAudio` implementation. See the [Speed](#Installation) section for more +details. ### Installation @@ -86,13 +86,12 @@ align="right" style="margin-left: 10px"/> This function works differently than the VQT from `librosa` or `nnAudio` in that it does not use the recursive downsampling algorithm from [this paper](http://academics.wellesley.edu/Physics/brown/pubs/effalgV92P2698-P2701.pdf). -Instead, it uses a fixed set of filters, and a Hilbert transform to compute the -analytic signal. It can then take the envelope and downsample. This results in a -more accurate computation of the spectrogram. The tradeoff is that under certain -conditions, it can be slower than the recursive downsampling approach, but -usually not by much. The direct computation approach also results in code that -is more flexible, easier to understand, and it has fewer constraints on the -input parameters compared to `librosa` and `nnAudio`. +Instead, it computes the power at each frequency using either direct- or +FFT-convolution with a filter bank of complex oscillations, followed by a +Hilbert transform. This results in a more accurate computation of the same +spectrogram. The direct computation approach also results in code that is more +flexible, easier to understand, and it has fewer constraints on the input +parameters compared to `librosa` and `nnAudio`. #### What to improve on? @@ -107,6 +106,10 @@ input parameters compared to `librosa` and `nnAudio`. - Currently, it is likely that the existing code is close to as fast as it can be without sacrificing accuracy, flexibility, or code clarity. All the important operations are done in PyTorch (with backends in `C` or `CUDA`). + - Recursive downsampling: Under many circumstances (like when `Q_high` is not + much greater than `Q_low`), recursive downsampling is fine. Implementing it + would be nice just for completeness ([from this + paper](http://academics.wellesley.edu/Physics/brown/pubs/effalgV92P2698-P2701.pdf)) - If we allow for some loss in accuracy: - For conv1d approach: Use a strided convolution. - For fftconv approach: Downsample using `n=n_samples_downsampled` in `ifft` @@ -130,20 +133,22 @@ import torch import matplotlib.pyplot as plt import scipy -data_ecg = scipy.datasets.electrocardiogram() +data_ecg = scipy.datasets.electrocardiogram()[:10000] -transformer = vqt.VQT( +my_vqt = vqt.VQT( Fs_sample=360, - Q_lowF=3, - Q_highF=20, + Q_lowF=2, + Q_highF=4, F_min=1, - F_max=180, - n_freq_bins=55, + F_max=120, + n_freq_bins=150, win_size=1501, - downsample_factor=8, + window_type='gaussian', + downsample_factor=4, padding='same', + fft_conv=True, return_complex=False, - plot_pref=True, + plot_pref=False, progressBar=False, ) @@ -152,9 +157,13 @@ specs, xaxis, freqs = transformer(data_ecg) fig, axs = plt.subplots(nrows=2, ncols=1, sharex=True, ) axs[0].plot(data_ecg) axs[0].title.set_text('Electrocardiogram') -axs[1].pcolor(xaxis, np.arange(specs[0].shape[0]), specs[0] * (freqs)[:, None]) -axs[1].set_yticks(np.arange(specs[0].shape[0])[::5], np.round(freqs[::5], 1)); -axs[1].set_xlim([43000, 48000]) +axs[1].pcolor( + xaxis, np.arange(specs[0].shape[0]), specs[0] * (freqs)[:, None], + vmin=0, vmax=30, + cmap='hot', +) +axs[1].set_yticks(np.arange(specs[0].shape[0])[::10], np.round(freqs[::10], 1)); +axs[1].set_xlim([5000, 7500]) axs[0].set_ylabel('mV') axs[1].set_ylabel('frequency (Hz)') axs[1].set_xlabel('time (s)') diff --git a/docs/media/example_ECG.png b/docs/media/example_ECG.png index f52a78c16d144c36597640734cda1daa32dce257..24e0ee6a527ffad4883de47e485f459f9f82c659 100644 GIT binary patch literal 126570 zcmeEuRa9I}*Cy^X4#6Q^+BZ$UtiK}d-T zseaTu(uGgK?wfDV%gZr=rIZ52Igd+XDV?Dq7+hf8po&H zeST-J0{=9oFVAfh*E)SMn>)6O>eKMwC77|29hk=3o=Of@_ zP?zpci2ighdSsk`e+xV$Cj;f!!2I{vpZHL~z*<6vk^eD}NN@o<(4UM90RigI$9EQ( 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