Identify abnormally elevated T waves as R waves

[Ginsakura]

figure 1 is hp.process() function 图1是hp.process()函数处理后的图表

wd, m = hp.process(self.healthMapList, self.sampleRate,clean_rr=True,clean_rr_method='z-score')
pfig = hp.plotter(wd, m,figsize=(20, 4),show=False)
pfig.subplots_adjust(**{"left":0.026,"bottom":0.07,"right":1,"top":1})
pfig.legend(prop={'family':'LXGW WenKai Mono','weight':'normal','size':12},framealpha=0.5)
plt.xlim(0,rrtime/1000+rrtime/1000000)

figure 2 is partial enlarged figure1 图2是局部放大的图1

figure 3 is Distribution of R-R interval (ibi) in time 图3是R-R间期(心跳间隔 Inter-Beat Interval IBI)在时间上的分布

wd, m = hp.process(self.healthMapList, self.sampleRate,clean_rr=True,clean_rr_method='z-score')
# rrwd = hp.clean_rr_intervals(working_data=wd)
rrfig = plt.figure('R-R与时间散点图',figsize=(20, 4))
rrfig.subplots_adjust(**{"left":0.026,"bottom":0.07,"right":1,"top":1})
rrlist,rrtimelist,rrtime = list(),list(),0
for ids in range(len(wd['RR_list'])):
    rrtime += wd['RR_list'][ids]
    if not wd['RR_masklist'][ids]:
        rrlist.append(wd['RR_list'][ids])
        rrtimelist.append(rrtime/1000)
print("正在绘制R-R与时间散点图中...")
plt.scatter(rrtimelist,rrlist,alpha=0.7,color='#8a68d0',s=5)
plt.plot(range(len(rrlist)),[sum(rrlist)/len(rrlist)]*len(rrlist),label=f"Avg R-R: {sum(rrlist)/len(rrlist)} ms",linestyle='--',alpha=0.5,linewidth=1,color='#CC0000')
plt.ylabel("ms")
plt.xlim(0,rrtime/1000+rrtime/1000000)
plt.legend(prop={'family':'LXGW WenKai Mono','weight':'normal','size':12},framealpha=0.5)

figure 4 is hp.plot_poincare() function. 图4是hp.plot_poincare()函数直接生成

with open(f'./{self.filename}/庞加莱散点图.txt','w') as f:
    f.write("%s\n%s"%(str(wd).replace(', ',',\n'),str(m).replace(', ',',\n')))
ppfig = hp.plot_poincare(wd, m,figsize=(10,10),show=False)
ppfig.subplots_adjust(**{"left":0.06,"bottom":0.045,"right":1,"top":0.97})
ppfig.savefig(f'./{self.filename}/庞加莱散点图.png')

Can abnormal T waves be filtered? 能否过滤异常T波？

[Ginsakura]

20230825_211455.zip This is ECG Record File It's a .rcd file ,not the .zip file. but,this file is read in binary mode, No need to pay attention to the extension 这是ECG记录文件 它并非zip文件,而是rcd文件 但是,这个文件是以二进制方式读取,无需在意扩展名是什么

The following is the file reading function Sampling Rate = 500 下面的是文件读取函数 采样率为500次/秒

healthMap = open(self.healthFilePath, 'rb')
healthMap.read(68)
healthMap.read(6)
healthMap.read(2)
self.SN = healthMap.read(18).decode()
print(f"设备序列号:{self.SN}")
healthMap.read(102)
self.Ver = healthMap.read(11).decode()
print(f"设备版本号:{self.Ver}")
healthMap.read(49)
self.startTime = int.from_bytes(healthMap.read(4), byteorder='little', signed=False)
self.endTime = int.from_bytes(healthMap.read(4), byteorder='little', signed=False)
print(f"start time:{self.startTime}\nend time:{self.endTime}")
healthMap.read(236)
while True:
    # 每个数据点2字节
    # 2 byte == 1 data point
    data = healthMap.read(2)
    if data == b'':break
    # 复位电压零点 12位AD值最大4096
    # 2048 == 0mV
    data = int.from_bytes(data, byteorder='little', signed=False)-2048
    data *= 0.00125 # Estimate sampling points and voltage values 估算的采样点与电压值
    self.healthMapList.append(data)
self.healthMapLenth = len(self.healthMapList)
print(f"数据点数目: {len(self.healthMapList)} 个")
print(f"数据持续时间: {len(self.healthMapList)/500} s")

healthMapList is sampling points list