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Classify ECG Signals Using DAG Network Deployed to FPGA

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This example shows how to classify human electrocardiogram (ECG) signals by deploying a transfer learning trained SqueezeNet network trainedSN to a Xilinx® Zynq® Ultrascale+™ ZCU102 board.

Required Products

For this example, you need:

  • Deep Learning Toolbox™

  • Image Processing Toolbox™

  • Wavelet Toolbox™

  • Deep Learning HDL Toolbox™

  • Deep Learning HDL Toolbox™ Support Package for Xilinx FPGA and SoC Devices

  • Xilinx Zynq Ultrascale+ MPSoC ZCu102

Download Data

Download the data from the GitHub repository. To download the data from the website, click Clone and select Download ZIP. Save the file physionet_ECG_data-main.zip in a folder where you have write permission.

After downloading the data from GitHub®, unzip the file in your temporary directory.

unzip(fullfile(tempdir,'physionet_ECG_data-main.zip'),tempdir);

The ECG data is classified into these labels:

  • persons with cardiac arrhythmia (ARR)

  • persons with congestive heart failure (CHF)

  • persons with normal sinus rhythms (NSR)

The data is collected from these sources:

Unzipping creates the folder physionet-ECG_data-main in your temporary directory.

Unzip ECGData.zip in physionet-ECG_data-main. Load the ECGData.mat data file into your MATLAB® workspace.

unzip(fullfile(tempdir,'physionet_ECG_data-main','ECGData.zip'),...
    fullfile(tempdir,'physionet_ECG_data-main'))
load(fullfile(tempdir,'physionet_ECG_data-main','ECGData.mat'))

Create a folder called dataDir inside the ECG data directory and then create three directories called ARR, CHF, and NSR inside dataDir by using the helperCreateECGDirectories function. You can find the source code for this helper function in the Supporting Functions section at the end of this example.

% parentDir = tempdir;
parentDir = pwd;
dataDir = 'data';
helperCreateECGDirectories(ECGData,parentDir,dataDir);

Plot an ECG that represents each ECG category by using the helperPlotReps helper function. You can find the source code for this helper function in the Supporting Functions section at the end of this example.

helperPlotReps(ECGData)

Create Time-Frequency Representations

After making the folders, create time-frequency representations of the ECG signals. Creating time-frequency representations helps with feature extraction. These representations are called scalograms. A scalogram is the absolute value of the continuous wavelet transform (CWT) coefficients of a signal. Create a CWT filter bank using cwtfilterbank for a signal with 1000 samples. 

Fs =128;
fb = cwtfilterbank(SignalLength=1000,...
    SamplingFrequency=Fs,...
    VoicesPerOctave=12);
sig = ECGData.Data(1,1:1000);
[cfs,frq] = wt(fb,sig);
t = (0:999)/Fs;figure;pcolor(t,frq,abs(cfs))
set(gca,'yscale','log');shading interp;axis tight;
title('Scalogram');xlabel('Time (s)');ylabel('Frequency (Hz)')

Use the helperCreateRGBfromTF helper function to create the scalograms as RGB images and write them to the appropriate subdirectory in dataDir. The source code for this helper function is in the Supporting Functions section at the end of this example. To be compatible with the SqueezeNet architecture, each RGB image is an array of size 227-by-227-by-3.

helperCreateRGBfromTF(ECGData,parentDir,dataDir)

Divide into Training and Validation Data

Load the scalogram images as an image datastore. The imageDatastore function automatically labels the images based on folder names and stores the data as an ImageDatastore object. An image datastore enables you to store large image data, including data that does not fit in memory, and efficiently read batches of images during training of a CNN.

allImages = imageDatastore(fullfile(parentDir,dataDir),...
    'IncludeSubfolders',true,...
    'LabelSource','foldernames');

Randomly divide the images into two groups. Use 80% of the images for training, and the remainder for validation. For purposes of reproducibility, we set the random seed to the default value.

rng default
[imgsTrain,imgsValidation] = splitEachLabel(allImages,0.8,'randomized');
disp(['Number of training images: ',num2str(numel(imgsTrain.Files))]);
disp(['Number of validation images: ',num2str(numel(imgsValidation.Files))]);

Load Transfer Learning Trained Network

Load the transfer learning trained SqueezeNet network trainedSN. To create the trainedSN network, see Classify Time Series Using Wavelet Analysis and Deep Learning (Deep Learning Toolbox).

load('trainedSN.mat');

Configure FPGA Board Interface

Configure the FPGA board interface for the deep learning network deployment and MATLAB communication by using the dlhdl.Target class to create a target object with a custom name for your target device and an interface to connect your target device to the host computer. To use JTAG,Install Xilinx® Vivado® Design Suite 2022.1. To set the Xilinx Vivado toolpath, enter:

% hdlsetuptoolpath('ToolName', 'Xilinx Vivado', 'ToolPath', 'C:\Xilinx\Vivado\2022.1\bin\vivado.bat');

hTarget = dlhdl.Target('Xilinx',Interface="Ethernet");

Prepare trainedSN Network for Deployment

Prepare the trainedSN network for deployment by using the dlhdl.Workflow class to create an object. When you create the object, specify the network and the bitstream name. Specify trainedSN as the network. Make sure that the bitstream name matches the data type and the FPGA board that you are targeting. In this example, the target FPGA board is the Xilinx ZCU102 SoC board. The bitstream uses a single data type.

hW=dlhdl.Workflow(Network=trainedSN,Bitstream='zcu102_single',Target=hTarget)
hW = 
  Workflow with properties:

            Network: [1×1 DAGNetwork]
          Bitstream: 'zcu102_single'
    ProcessorConfig: []
             Target: [1×1 dnnfpga.hardware.TargetEthernet]

Generate Weights, Biases, and Instructions

Generate weights, biases, and instructions for the trainedSN network by using the compile method of the dlhdl.Workflow object. 

dn = hW.compile          
### Compiling network for Deep Learning FPGA prototyping ...
### Targeting FPGA bitstream zcu102_single.
### The network includes the following layers:
     1   'data'                    Image Input                  227×227×3 images with 'zerocenter' normalization                     (SW Layer)
     2   'conv1'                   Convolution                  64 3×3×3 convolutions with stride [2  2] and padding [0  0  0  0]    (HW Layer)
     3   'relu_conv1'              ReLU                         ReLU                                                                 (HW Layer)
     4   'pool1'                   Max Pooling                  3×3 max pooling with stride [2  2] and padding [0  0  0  0]          (HW Layer)
     5   'fire2-squeeze1x1'        Convolution                  16 1×1×64 convolutions with stride [1  1] and padding [0  0  0  0]   (HW Layer)
     6   'fire2-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
     7   'fire2-expand1x1'         Convolution                  64 1×1×16 convolutions with stride [1  1] and padding [0  0  0  0]   (HW Layer)
     8   'fire2-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
     9   'fire2-expand3x3'         Convolution                  64 3×3×16 convolutions with stride [1  1] and padding [1  1  1  1]   (HW Layer)
    10   'fire2-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    11   'fire2-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    12   'fire3-squeeze1x1'        Convolution                  16 1×1×128 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    13   'fire3-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
    14   'fire3-expand1x1'         Convolution                  64 1×1×16 convolutions with stride [1  1] and padding [0  0  0  0]   (HW Layer)
    15   'fire3-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
    16   'fire3-expand3x3'         Convolution                  64 3×3×16 convolutions with stride [1  1] and padding [1  1  1  1]   (HW Layer)
    17   'fire3-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    18   'fire3-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    19   'pool3'                   Max Pooling                  3×3 max pooling with stride [2  2] and padding [0  1  0  1]          (HW Layer)
    20   'fire4-squeeze1x1'        Convolution                  32 1×1×128 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    21   'fire4-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
    22   'fire4-expand1x1'         Convolution                  128 1×1×32 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    23   'fire4-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
    24   'fire4-expand3x3'         Convolution                  128 3×3×32 convolutions with stride [1  1] and padding [1  1  1  1]  (HW Layer)
    25   'fire4-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    26   'fire4-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    27   'fire5-squeeze1x1'        Convolution                  32 1×1×256 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    28   'fire5-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
    29   'fire5-expand1x1'         Convolution                  128 1×1×32 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    30   'fire5-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
    31   'fire5-expand3x3'         Convolution                  128 3×3×32 convolutions with stride [1  1] and padding [1  1  1  1]  (HW Layer)
    32   'fire5-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    33   'fire5-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    34   'pool5'                   Max Pooling                  3×3 max pooling with stride [2  2] and padding [0  1  0  1]          (HW Layer)
    35   'fire6-squeeze1x1'        Convolution                  48 1×1×256 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    36   'fire6-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
    37   'fire6-expand1x1'         Convolution                  192 1×1×48 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    38   'fire6-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
    39   'fire6-expand3x3'         Convolution                  192 3×3×48 convolutions with stride [1  1] and padding [1  1  1  1]  (HW Layer)
    40   'fire6-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    41   'fire6-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    42   'fire7-squeeze1x1'        Convolution                  48 1×1×384 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    43   'fire7-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
    44   'fire7-expand1x1'         Convolution                  192 1×1×48 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    45   'fire7-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
    46   'fire7-expand3x3'         Convolution                  192 3×3×48 convolutions with stride [1  1] and padding [1  1  1  1]  (HW Layer)
    47   'fire7-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    48   'fire7-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    49   'fire8-squeeze1x1'        Convolution                  64 1×1×384 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    50   'fire8-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
    51   'fire8-expand1x1'         Convolution                  256 1×1×64 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    52   'fire8-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
    53   'fire8-expand3x3'         Convolution                  256 3×3×64 convolutions with stride [1  1] and padding [1  1  1  1]  (HW Layer)
    54   'fire8-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    55   'fire8-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    56   'fire9-squeeze1x1'        Convolution                  64 1×1×512 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    57   'fire9-relu_squeeze1x1'   ReLU                         ReLU                                                                 (HW Layer)
    58   'fire9-expand1x1'         Convolution                  256 1×1×64 convolutions with stride [1  1] and padding [0  0  0  0]  (HW Layer)
    59   'fire9-relu_expand1x1'    ReLU                         ReLU                                                                 (HW Layer)
    60   'fire9-expand3x3'         Convolution                  256 3×3×64 convolutions with stride [1  1] and padding [1  1  1  1]  (HW Layer)
    61   'fire9-relu_expand3x3'    ReLU                         ReLU                                                                 (HW Layer)
    62   'fire9-concat'            Depth concatenation          Depth concatenation of 2 inputs                                      (HW Layer)
    63   'new_dropout'             Dropout                      60% dropout                                                          (HW Layer)
    64   'new_conv'                Convolution                  3 1×1×512 convolutions with stride [1  1] and padding [0  0  0  0]   (HW Layer)
    65   'relu_conv10'             ReLU                         ReLU                                                                 (HW Layer)
    66   'pool10'                  2-D Global Average Pooling   2-D global average pooling                                           (HW Layer)
    67   'prob'                    Softmax                      softmax                                                              (HW Layer)
    68   'new_classoutput'         Classification Output        crossentropyex with 'ARR' and 2 other classes                        (SW Layer)
                                                                                                                                   
### Notice: The layer 'data' of type 'ImageInputLayer' is split into an image input layer 'data' and an addition layer 'data_norm' for normalization on hardware.
### Notice: The layer 'prob' with type 'nnet.cnn.layer.SoftmaxLayer' is implemented in software.
### Notice: The layer 'new_classoutput' with type 'nnet.cnn.layer.ClassificationOutputLayer' is implemented in software.
### Compiling layer group: conv1>>fire2-relu_squeeze1x1 ...
### Compiling layer group: conv1>>fire2-relu_squeeze1x1 ... complete.
### Compiling layer group: fire2-expand1x1>>fire2-relu_expand1x1 ...
### Compiling layer group: fire2-expand1x1>>fire2-relu_expand1x1 ... complete.
### Compiling layer group: fire2-expand3x3>>fire2-relu_expand3x3 ...
### Compiling layer group: fire2-expand3x3>>fire2-relu_expand3x3 ... complete.
### Compiling layer group: fire3-squeeze1x1>>fire3-relu_squeeze1x1 ...
### Compiling layer group: fire3-squeeze1x1>>fire3-relu_squeeze1x1 ... complete.
### Compiling layer group: fire3-expand1x1>>fire3-relu_expand1x1 ...
### Compiling layer group: fire3-expand1x1>>fire3-relu_expand1x1 ... complete.
### Compiling layer group: fire3-expand3x3>>fire3-relu_expand3x3 ...
### Compiling layer group: fire3-expand3x3>>fire3-relu_expand3x3 ... complete.
### Compiling layer group: pool3>>fire4-relu_squeeze1x1 ...
### Compiling layer group: pool3>>fire4-relu_squeeze1x1 ... complete.
### Compiling layer group: fire4-expand1x1>>fire4-relu_expand1x1 ...
### Compiling layer group: fire4-expand1x1>>fire4-relu_expand1x1 ... complete.
### Compiling layer group: fire4-expand3x3>>fire4-relu_expand3x3 ...
### Compiling layer group: fire4-expand3x3>>fire4-relu_expand3x3 ... complete.
### Compiling layer group: fire5-squeeze1x1>>fire5-relu_squeeze1x1 ...
### Compiling layer group: fire5-squeeze1x1>>fire5-relu_squeeze1x1 ... complete.
### Compiling layer group: fire5-expand1x1>>fire5-relu_expand1x1 ...
### Compiling layer group: fire5-expand1x1>>fire5-relu_expand1x1 ... complete.
### Compiling layer group: fire5-expand3x3>>fire5-relu_expand3x3 ...
### Compiling layer group: fire5-expand3x3>>fire5-relu_expand3x3 ... complete.
### Compiling layer group: pool5>>fire6-relu_squeeze1x1 ...
### Compiling layer group: pool5>>fire6-relu_squeeze1x1 ... complete.
### Compiling layer group: fire6-expand1x1>>fire6-relu_expand1x1 ...
### Compiling layer group: fire6-expand1x1>>fire6-relu_expand1x1 ... complete.
### Compiling layer group: fire6-expand3x3>>fire6-relu_expand3x3 ...
### Compiling layer group: fire6-expand3x3>>fire6-relu_expand3x3 ... complete.
### Compiling layer group: fire7-squeeze1x1>>fire7-relu_squeeze1x1 ...
### Compiling layer group: fire7-squeeze1x1>>fire7-relu_squeeze1x1 ... complete.
### Compiling layer group: fire7-expand1x1>>fire7-relu_expand1x1 ...
### Compiling layer group: fire7-expand1x1>>fire7-relu_expand1x1 ... complete.
### Compiling layer group: fire7-expand3x3>>fire7-relu_expand3x3 ...
### Compiling layer group: fire7-expand3x3>>fire7-relu_expand3x3 ... complete.
### Compiling layer group: fire8-squeeze1x1>>fire8-relu_squeeze1x1 ...
### Compiling layer group: fire8-squeeze1x1>>fire8-relu_squeeze1x1 ... complete.
### Compiling layer group: fire8-expand1x1>>fire8-relu_expand1x1 ...
### Compiling layer group: fire8-expand1x1>>fire8-relu_expand1x1 ... complete.
### Compiling layer group: fire8-expand3x3>>fire8-relu_expand3x3 ...
### Compiling layer group: fire8-expand3x3>>fire8-relu_expand3x3 ... complete.
### Compiling layer group: fire9-squeeze1x1>>fire9-relu_squeeze1x1 ...
### Compiling layer group: fire9-squeeze1x1>>fire9-relu_squeeze1x1 ... complete.
### Compiling layer group: fire9-expand1x1>>fire9-relu_expand1x1 ...
### Compiling layer group: fire9-expand1x1>>fire9-relu_expand1x1 ... complete.
### Compiling layer group: fire9-expand3x3>>fire9-relu_expand3x3 ...
### Compiling layer group: fire9-expand3x3>>fire9-relu_expand3x3 ... complete.
### Compiling layer group: new_conv>>pool10 ...
### Compiling layer group: new_conv>>pool10 ... complete.

### Allocating external memory buffers:

          offset_name          offset_address    allocated_space 
    _______________________    ______________    ________________

    "InputDataOffset"           "0x00000000"     "24.0 MB"       
    "OutputResultOffset"        "0x01800000"     "4.0 MB"        
    "SchedulerDataOffset"       "0x01c00000"     "4.0 MB"        
    "SystemBufferOffset"        "0x02000000"     "28.0 MB"       
    "InstructionDataOffset"     "0x03c00000"     "4.0 MB"        
    "ConvWeightDataOffset"      "0x04000000"     "12.0 MB"       
    "EndOffset"                 "0x04c00000"     "Total: 76.0 MB"

### Network compilation complete.

dn = struct with fields:
             weights: [1×1 struct]
        instructions: [1×1 struct]
           registers: [1×1 struct]
    syncInstructions: [1×1 struct]
        constantData: {{}  [-24.2516 -50.7900 -184.4480 0 -24.2516 -50.7900 -184.4480 0 -24.2516 -50.7900 -184.4480 0 -24.2516 -50.7900 -184.4480 0 -24.2516 -50.7900 -184.4480 0 -24.2516 -50.7900 -184.4480 0 -24.2516 -50.7900 -184.4480 0 -24.2516 … ]}

Program Bitstream onto FPGA and Download Network Weights

To deploy the network on the Xilinx ZCU102 hardware, run the deploy function of the dlhdl.Workflow object. This function uses the output of the compile function to program the FPGA board by using the programming file. It also downloads the network weights and biases. The deploy function starts programming the FPGA device, displays progress messages, and the time it takes to deploy the network.

hW.deploy
### Programming FPGA Bitstream using Ethernet...
### Attempting to connect to the hardware board at 192.168.1.101...
### Connection successful
### Programming FPGA device on Xilinx SoC hardware board at 192.168.1.101...
### Copying FPGA programming files to SD card...
### Setting FPGA bitstream and devicetree for boot...
# Copying Bitstream zcu102_single.bit to /mnt/hdlcoder_rd
# Set Bitstream to hdlcoder_rd/zcu102_single.bit
# Copying Devicetree devicetree_dlhdl.dtb to /mnt/hdlcoder_rd
# Set Devicetree to hdlcoder_rd/devicetree_dlhdl.dtb
# Set up boot for Reference Design: 'AXI-Stream DDR Memory Access : 3-AXIM'
### Rebooting Xilinx SoC at 192.168.1.101...
### Reboot may take several seconds...
### Attempting to connect to the hardware board at 192.168.1.101...
### Connection successful
### Programming the FPGA bitstream has been completed successfully.
### Loading weights to Conv Processor.
### Conv Weights loaded. Current time is 28-Apr-2022 15:33:54

Load Image for Prediction and Run Prediction

Load an image by randomly selecting an image from the validation data store.

idx=randi(32);
testim=readimage(imgsValidation,idx);
imshow(testim)

Execute the predict method on the dlhdl.Workflow object and then show the label in the MATLAB command window.

[YPred1,probs1] = classify(trainedSN,testim);
accuracy1 = (YPred1==imgsValidation.Labels);
[YPred2,probs2] = hW.predict(single(testim),'profile','on');
### Finished writing input activations.
### Running single input activation.


              Deep Learning Processor Profiler Performance Results

                   LastFrameLatency(cycles)   LastFrameLatency(seconds)       FramesNum      Total Latency     Frames/s
                         -------------             -------------              ---------        ---------       ---------
Network                    9253245                  0.04206                       1            9257253             23.8
    data_norm               361047                  0.00164 
    conv1                   672559                  0.00306 
    pool1                   509079                  0.00231 
    fire2-squeeze1x1        308258                  0.00140 
    fire2-expand1x1         305646                  0.00139 
    fire2-expand3x3         305085                  0.00139 
    fire3-squeeze1x1        627799                  0.00285 
    fire3-expand1x1         305241                  0.00139 
    fire3-expand3x3         305256                  0.00139 
    pool3                   286627                  0.00130 
    fire4-squeeze1x1        264151                  0.00120 
    fire4-expand1x1         264600                  0.00120 
    fire4-expand3x3         264567                  0.00120 
    fire5-squeeze1x1        734588                  0.00334 
    fire5-expand1x1         264575                  0.00120 
    fire5-expand3x3         264719                  0.00120 
    pool5                   219725                  0.00100 
    fire6-squeeze1x1        194605                  0.00088 
    fire6-expand1x1         144199                  0.00066 
    fire6-expand3x3         144819                  0.00066 
    fire7-squeeze1x1        288819                  0.00131 
    fire7-expand1x1         144285                  0.00066 
    fire7-expand3x3         144841                  0.00066 
    fire8-squeeze1x1        368116                  0.00167 
    fire8-expand1x1         243691                  0.00111 
    fire8-expand3x3         243738                  0.00111 
    fire9-squeeze1x1        488338                  0.00222 
    fire9-expand1x1         243654                  0.00111 
    fire9-expand3x3         243683                  0.00111 
    new_conv                 93849                  0.00043 
    pool10                    2751                  0.00001 
 * The clock frequency of the DL processor is: 220MHz

[val,idx]= max(YPred2);
trainedSN.Layers(end).ClassNames{idx}
ans = 
'ARR'

References

  1. Baim, D. S., W. S. Colucci, E. S. Monrad, H. S. Smith, R. F. Wright, A. Lanoue, D. F. Gauthier, B. J. Ransil, W. Grossman, and E. Braunwald. "Survival of patients with severe congestive heart failure treated with oral milrinone." Journal of the American College of Cardiology. Vol. 7, Number 3, 1986, pp. 661–670.

  2. Engin, M. "ECG beat classification using neuro-fuzzy network." Pattern Recognition Letters. Vol. 25, Number 15, 2004, pp.1715–1722.

  3. Goldberger A. L., L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley. "PhysioBank, PhysioToolkit,and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals." Circulation. Vol. 101, Number 23: e215–e220. [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13). doi: 10.1161/01.CIR.101.23.e215.

  4. Leonarduzzi, R. F., G. Schlotthauer, and M. E. Torres. "Wavelet leader based multifractal analysis of heart rate variability during myocardial ischaemia." In Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE, 110–113. Buenos Aires, Argentina: IEEE, 2010.

  5. Li, T., and M. Zhou. "ECG classification using wavelet packet entropy and random forests." Entropy. Vol. 18, Number 8, 2016, p.285.

  6. Maharaj, E. A., and A. M. Alonso. "Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals." Computational Statistics and Data Analysis. Vol. 70, 2014, pp. 67–87.

  7. Moody, G. B., and R. G. Mark. "The impact of the MIT-BIH Arrhythmia Database." IEEE Engineering in Medicine and Biology Magazine. Vol. 20. Number 3, May-June 2001, pp. 45–50. (PMID: 11446209)

  8. Russakovsky, O., J. Deng, and H. Su et al. "ImageNet Large Scale Visual Recognition Challenge." International Journal of Computer Vision. Vol. 115, Number 3, 2015, pp. 211–252.

  9. Zhao, Q., and L. Zhang. "ECG feature extraction and classification using wavelet transform and support vector machines." In IEEE International Conference on Neural Networks and Brain, 1089–1092. Beijing, China: IEEE, 2005.

  10. ImageNet. http://www.image-net.org

Supporting Functions

helperCreateECGDataDirectories creates a data directory inside a parent directory, then creates three subdirectories inside the data directory. The subdirectories are named after each class of ECG signal found in ECGData.

function helperCreateECGDirectories(ECGData,parentFolder,dataFolder)

rootFolder = parentFolder;
localFolder = dataFolder;
mkdir(fullfile(rootFolder,localFolder))

folderLabels = unique(ECGData.Labels);
for i = 1:numel(folderLabels)
    mkdir(fullfile(rootFolder,localFolder,char(folderLabels(i))));
end
end

helperPlotReps plots the first thousand samples of a representative of each class of ECG signal found in ECGData.

function helperPlotReps(ECGData)

folderLabels = unique(ECGData.Labels);

for k=1:3
    ecgType = folderLabels{k};
    ind = find(ismember(ECGData.Labels,ecgType));
    subplot(3,1,k)
    plot(ECGData.Data(ind(1),1:1000));
    grid on
    title(ecgType)
end
end

helperCreateRGBfromTF uses cwtfilterbank to obtain the continuous wavelet transform of the ECG signals and generates the scalograms from the wavelet coefficients. The helper function resizes the scalograms and writes them to disk as jpeg images.

function helperCreateRGBfromTF(ECGData,parentFolder,childFolder)

imageRoot = fullfile(parentFolder,childFolder);

data = ECGData.Data;
labels = ECGData.Labels;

[~,signalLength] = size(data);

fb = cwtfilterbank('SignalLength',signalLength,'VoicesPerOctave',12);
r = size(data,1);

for ii = 1:r
    cfs = abs(fb.wt(data(ii,:)));
    im = ind2rgb(im2uint8(rescale(cfs)),jet(128));
    
    imgLoc = fullfile(imageRoot,char(labels(ii)));
    imFileName = strcat(char(labels(ii)),'_',num2str(ii),'.jpg');
    imwrite(imresize(im,[227 227]),fullfile(imgLoc,imFileName));
end
end

See Also

Related Topics