biosignalsnotebooks | project logo [main files] Signal Samples Library

With PLUX acquisition systems, a vast set of physiological signals can be acquired.

All the signals that were used in biosignalsnotebooks notebooks have been collected with bitalino or biosignalsplux , being this page a resource where relevant characteristics of each acquisition are presented, together with a temporal segment of the signal.

In [1]:
import biosignalsnotebooks as bsnb
import numpy

# Base packages used in OpenSignals Tools Notebooks for plotting data
from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from bokeh.layouts import gridplot
from bokeh.models.tools import *
output_notebook(hide_banner=True)
bvp_sample
Signal Type BVP
Acquisition Time 00:27.3
Sample Rate 1000 Hz
Number of Channels 1
Resolutions 16 bits
Observations At Rest
In [2]:
signal_dict, file_header = bsnb.load("//biosignalsplux.com/signal_samples/" + "bvp_sample.txt", get_header=True, out_dict=True)
mac_addresses = list(signal_dict.keys())

mac_0 = mac_addresses[0]
chn_0 = list(signal_dict[mac_0].keys())[0]
sample_rate = file_header[mac_0]["sampling rate"]
time = numpy.linspace(0, len(signal_dict[mac_0][chn_0]) / sample_rate,
                      len(signal_dict[mac_0][chn_0]))
grid_layout = []
for mac in mac_addresses:
    channels = list(signal_dict[mac].keys())
    for chn in channels:
        fig = figure(x_axis_label='Time (s)', y_axis_label='Raw Data',
                     title=mac + "@" + chn, **bsnb.opensignals_kwargs("figure"))
        fig.line(time, signal_dict[mac][chn],
                 **bsnb.opensignals_kwargs("line"))
        grid_layout.append([fig])
bsnb.opensignals_style([item for sublist in grid_layout for item in sublist])
grid_plot = gridplot(grid_layout, **bsnb.opensignals_kwargs("gridplot"))
show(grid_plot)
ecg_20_sec_1000_Hz
Signal Type ECG
Acquisition Time 00:20.4
Sample Rate 1000 Hz
Number of Channels 1
Resolutions 16 bits
Observations At Rest using Lead II
In [3]:
signal_dict, file_header = bsnb.load("//biosignalsplux.com/signal_samples/" + "ecg_20_sec_1000_Hz.h5", get_header=True, out_dict=True)
mac_addresses = list(signal_dict.keys())

mac_0 = mac_addresses[0]
chn_0 = list(signal_dict[mac_0].keys())[0]
sample_rate = file_header[mac_0]["sampling rate"]
time = numpy.linspace(0, len(signal_dict[mac_0][chn_0]) / sample_rate,
                      len(signal_dict[mac_0][chn_0]))
grid_layout = []
for mac in mac_addresses:
    channels = list(signal_dict[mac].keys())
    for chn in channels:
        fig = figure(x_axis_label='Time (s)', y_axis_label='Raw Data',
                     title=mac + "@" + chn, **bsnb.opensignals_kwargs("figure"))
        fig.line(time, signal_dict[mac][chn],
                 **bsnb.opensignals_kwargs("line"))
        grid_layout.append([fig])
bsnb.opensignals_style([item for sublist in grid_layout for item in sublist])
grid_plot = gridplot(grid_layout, **bsnb.opensignals_kwargs("gridplot"))
show(grid_plot)