File size: 3,174 Bytes
458c8e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # Import necessary libraries
import dash
from dash import html, dcc
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Load the dataset
file_path = 'dataset/JS00001_filtered.csv' # Update this path as necessary
ecg_data = pd.read_csv(file_path)
# Define leads
leads = ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
# Initialize the Dash app
app = dash.Dash(__name__)
app.layout = html.Div(children=[
html.H1(children='ECG Data Dashboard', style={'textAlign': 'center', 'color': '#007BFF'}),
html.Div([
html.Label('Select ECG Lead for Analysis:'),
dcc.Dropdown(
id='lead-dropdown',
options=[{'label': 'All Leads', 'value': 'ALL'}] + [{'label': lead, 'value': lead} for lead in leads],
value='ALL' # Default value to show all leads
)
], style={'width': '30%', 'margin': '0 auto', 'padding': '20px'}),
dcc.Graph(id='ecg-data-visualization'),
], style={'padding': '20px', 'maxWidth': '1200px', 'margin': '0 auto'})
# Define callback to update the figure based on the selected lead
@app.callback(
Output('ecg-data-visualization', 'figure'),
[Input('lead-dropdown', 'value')]
)
def update_figure(selected_lead):
# Initialize the figure with subplots
fig = make_subplots(rows=4, cols=1,
subplot_titles=("ECG Signal Over Time", "Histogram of Signal Amplitudes",
"Scatter Plot: Lead I vs Lead II", "Rolling Average"),
vertical_spacing=0.1,
specs=[[{"type": "scatter"}], [{"type": "histogram"}], [{"type": "scatter"}], [{"type": "scatter"}]])
# Conditionally display either all leads or just the selected lead
if selected_lead == 'ALL':
# Show all leads
for lead in leads:
fig.add_trace(go.Scatter(x=ecg_data['time'], y=ecg_data[lead], mode='lines', name=f'Lead {lead}'), row=1, col=1)
fig.add_trace(go.Histogram(x=ecg_data[lead], name=f'Lead {lead}', opacity=0.75), row=2, col=1)
else:
# Show only the selected lead
ecg_data['RollingAvg'] = ecg_data[selected_lead].rolling(window=100).mean()
fig.add_trace(go.Scatter(x=ecg_data['time'], y=ecg_data[selected_lead], mode='lines', name=f'Lead {selected_lead}'), row=1, col=1)
fig.add_trace(go.Histogram(x=ecg_data[selected_lead], name=f'Lead {selected_lead}', opacity=0.75), row=2, col=1)
fig.add_trace(go.Scatter(x=ecg_data['time'], y=ecg_data['RollingAvg'], mode='lines', name=f'Rolling Average: Lead {selected_lead}'), row=4, col=1)
# Common settings for all cases
fig.update_traces(opacity=0.75, bingroup=1, row=2, col=1)
fig.update_layout(barmode='overlay')
fig.add_trace(go.Scatter(x=ecg_data['I'], y=ecg_data['II'], mode='markers', name='Lead I vs Lead II'), row=3, col=1)
# Update the figure layout
fig.update_layout(height=1600, title_text="Comprehensive ECG Data Analysis", showlegend=True)
return fig
# Run the app
if __name__ == '__main__':
app.run_server(debug=True) |