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实验室版 API

实验室版提供ECGFounder、文本生成单导联、单导联生成十二导联、三导联生成十二导联、PPG信号生成单导联以及单导联生成心脏超声的核心能力。

注意事项

🔬 欢迎使用实验室 API!
目前所有接口均处于公测阶段,功能可能会不定期调整。
为确保正常访问和数据安全,请进入 科研合作页面 联系工作人员获取专属访问密钥。
💡 温馨提示:请妥善保管您的密钥,避免泄露或滥用。

适用场景
  • 医学研究
  • 心电模型验证

MCMA接口

POST /api/v1/experimental/mcma

通过输入单导联信号可以生成十二导联信号。

请求参数

参数类型必填说明
ecgDataarrayECG信号数据数组(ADC值)
ecgSampleratenumber采样率,单位Hz(推荐250-500)
originalboolean是否输出原始波形,默认true

请求示例

curl -X POST "https://api.heartvoice.com.cn/api/v1/experimental/mcma" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"ecgData": [512, 515, 520, 518, 525, ...],
"ecgSampleRate": 500
}'

响应示例

{
"errorCode": "0",
"msg": "成功",
"data": [
[
-0.0005600000149570405,
0.05680999904870987,
-0.002580000087618828,
0.02824000082910061,
...
]
]
}

可视化展示

import ecg_plot
ecg_plot.plot(gen_ecg12, sample_rate=fs, title='ECG 12')
ecg_plot.show()

文献

@article{chen2024multi,
title={Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG},
author={Chen, Jiarong and Wu, Wanqing and Liu, Tong and Hong, Shenda},
journal={npj Cardiovascular Health},
volume={1},
number={1},
pages={34},
year={2024},
publisher={Nature Publishing Group UK London}
}
Chen, J., Wu, W., Liu, T., & Hong, S. (2024). Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG. npj Cardiovascular Health, 1(1), 34.

DiffuSETS接口

POST /api/v1/experimental/diffuSets

通过输入单导联信号可以生成十二导联信号。

请求参数

参数类型必填说明
textstr临床诊断文本,多个诊断需要用'|'分隔开
ageint年龄
sexstr性别
hrint心率
batchint选择生成ECG的数量

请求示例

curl -X POST "https://api.heartvoice.com.cn/api/v1/experimental/diffuSets" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"text": 'Sinus rhythm|Normal ECG.', # Clinical text report, multi-reports should be split by '|'
"age": 50, # Age of patient
"sex": 'M',
"hr": 80,
'batch': 1
}'

响应示例

{
"errorCode": "0",
"msg": "成功",
"data": {
"textImg": "[\"iVBORw0KGgoAAAANSUhI=...\"]"
}
}

可视化展示

import time
import requests
import json
import base64
import io
from PIL import Image

def base64_to_image(base64_str):
# 解码Base64字符串
image_data = base64.b64decode(base64_str)
# 使用io.BytesIO将解码后的数据转换为文件对象
image_file = io.BytesIO(image_data)
# 使用PIL打开图片
image = Image.open(image_file)
return image

ecg_img = base64_to_image(ecg_data)
ecg_img.show()

文献

@article{lai2025diffusets,
title={DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information},
author={Lai, Yongfan and Chen, Jiabo and Zhao, Qinghao and Zhang, Deyun and Wang, Yue and Geng, Shijia and Li, Hongyan and Hong, Shenda},
journal={Patterns},
year={2025},
publisher={Elsevier}
}
Lai, Y., Chen, J., Zhao, Q., Zhang, D., Wang, Y., Geng, S., ... & Hong, S. (2025). DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information. Patterns.

WearECG接口

POST /api/v1/experimental/lead3Lead12

通过输入单导联信号可以生成十二导联信号。

请求参数

参数类型必填说明
ecgDataarrayECG信号数据数组(ADC值)
ecgSampleRatenumber采样率,单位Hz(推荐250-500)

请求示例

curl -X POST "https://api.heartvoice.com.cn/api/v1/experimental/lead3Lead12" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"ecgData": [[512, 515, 520, 518, 525, ...],[512, 515, 520, 518, 525, ...],[512, 515, 520, 518, 525, ...]],
"ecgSampleRate": 500
}'

响应示例

{
"errorCode": "0",
"msg": "成功",
"data": [
[
-0.0025084596127271652,
0.0118071788037084,
0.014166647854523678,
...
]
]
}

可视化展示

import ecg_plot
ecg_plot.plot(gen_ecg12, sample_rate=fs, title='ECG 12')
ecg_plot.show()


PPGFlowECG接口

POST /api/v1/experimental/ppgFlowEcg

通过输入单导联信号可以生成十二导联信号。

请求参数

参数类型必填说明
ppgDataarrayPPG信号数据数组。上传红光、绿光或者红外光数据
ppgSampleRatenumber采样率,单位Hz(推荐250-500)

请求示例

curl -X POST "https://api.heartvoice.com.cn/api/v1/experimental/ppgFlowEcg" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"ppgData": [512, 515, 520, 518, 525, ...],
"ecgSampleRate": 500
}'

响应示例

{
"errorCode": "0",
"msg": "成功",
"data": [
[
-0.0005600000149570405,
0.05680999904870987,
-0.002580000087618828,
0.02824000082910061,
...
]
]
}

可视化展示

文献

@article{fang2025ppgflowecg,
title={PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection},
author={Fang, Xiaocheng and Jin, Jiarui and Wang, Haoyu and Liu, Che and Cai, Jieyi and Nie, Guangkun and Li, Jun and Li, Hongyan and Hong, Shenda},
journal={arXiv preprint arXiv:2509.19774},
year={2025}
}
Fang, X., Jin, J., Wang, H., Liu, C., Cai, J., Nie, G., ... & Hong, S. (2025). PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection. arXiv preprint arXiv:2509.19774.

ECHOPulse接口

POST /api/v1/experimental/ecgEcho

通过输入单导联信号可以生成十二导联信号。

请求参数

参数类型必填说明
ecgDataarrayECG信号数据数组(ADC值)
ecgSampleRatenumber采样率,单位Hz(推荐250-500)

请求示例

curl -X POST "https://api.heartvoice.com.cn/api/v1/experimental/ecgEcho" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"ecgData": [512, 515, 520, 518, 525, ...],
"ecgSampleRate": 500
}'

响应示例

{
"errorCode": "0",
"msg": "成功",
"data": {
"ecgEcho": [
[
[
[
[
0.0013988255523145199,
0.004246973432600498,
0.0010940954089164734,
0.004930938594043255,
-0.0024434151127934456,
0.00477356743067503,
...
]
]
]
]
]
}
}

可视化展示

保存ECHO的视频帧
def save_video_and_plot_frames(video_tensor, save_dir='output'):
os.makedirs(save_dir, exist_ok=True)
T = video_tensor.shape[2] # 时间维度
fig, axes = plt.subplots(1, T, figsize=(T * 3, 3))

for t in range(T):
# 取出第 t 帧
frame = video_tensor[0, :, t, :, :] # [C, H, W]

# 转成 [H, W, C] numpy
frame_np = frame.permute(1, 2, 0).cpu().numpy()

# 归一化到 [0,1] 避免 imshow clipping
frame_min, frame_max = frame_np.min(), frame_np.max()
if frame_max > frame_min:
frame_np = (frame_np - frame_min) / (frame_max - frame_min)
else:
frame_np = np.zeros_like(frame_np) # 防止除零

ax = axes[t]
ax.imshow(frame_np)
ax.set_title(f'Frame {t}')
ax.axis('off')

for i in range(T, len(axes)):
axes[i].axis('off')

plt.tight_layout()
plt.savefig(os.path.join(save_dir, 'echo_frames.png'))
plt.show()
plt.close()

print(f"All frames plotted in {save_dir}")
保存结果为gif
def video_tensor_to_gif_with_ecg(video_tensor, ecg_data, output_path, title, duration=125, fps=8):
# 将视频内容转换成numpy阵列
video_np = np.array(video_tensor)
video_np = np.squeeze(video_np, axis=0)
# 改变维度顺序 (C, T, H, W) -> (T, H, W, C)
video_np = np.transpose(video_np, (1, 2, 3, 0))

# EKG 数据准备(缩小维度)
ecg = np.array(ecg_data)
ecg = ecg[0, 1, :] # Assuming this is the correct dimension for EKG data

# 确认帧数
num_frames = 11
# EKG 数据根据帧数重新抽样
x_original = np.linspace(0, 1, len(ecg))
x_resampled = np.linspace(0, 1, num_frames)
ecg_resampled = np.interp(x_resampled, x_original, ecg)

# 设定画布
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10), gridspec_kw={'height_ratios': [3, 1]})

# 设置初始化图像
im = ax1.imshow(np.zeros((128, 128, 3)), animated=True)
ax1.set_title(title)
line, = ax2.plot([], [], 'r-')

ax1.axis('off')
ax2.set_xlim(0, num_frames)
ax2.set_ylim(ecg_resampled.min(), ecg_resampled.max())
ax2.set_xlabel('Frame')
ax2.set_ylabel('EKG')
ax2.set_title('Lead I EKG')

def update(frame):
im.set_array(video_np[frame])
line.set_data(range(frame + 1), ecg_resampled[:frame + 1])
return im, line

num_frames = video_np.shape[0]
anim = FuncAnimation(fig, update, frames=num_frames, interval=duration, blit=True)

anim.save(output_path, writer='pillow', fps=fps)
plt.close(fig)

print(f"GIF saved to {output_path}")

可视化展示

  • 生成的ECHO帧(调用save_video_and_plot_frames)

  • 转成gif(调用video_tensor_to_gif_with_ecg)

文献

@article{li2024echopulse,
title={Echopulse: Ecg controlled echocardio-grams video generation},
author={Li, Yiwei and Kim, Sekeun and Wu, Zihao and Jiang, Hanqi and Pan, Yi and Jin, Pengfei and Song, Sifan and Shi, Yucheng and Liu, Tianming and Li, Quanzheng and others},
journal={arXiv preprint arXiv:2410.03143},
year={2024}
}
Li, Y., Kim, S., Wu, Z., Jiang, H., Pan, Y., Jin, P., ... & Li, X. (2024). Echopulse: Ecg controlled echocardio-grams video generation. arXiv preprint arXiv:2410.03143.

ECGTwins接口

POST /api/v1/experimental/ecgTwins

通过输入单导联信号可以生成十二导联信号。

请求参数

参数类型必填说明
reference_filefile参考文件,目前是固定的 示例文件
textstr临床诊断文本,多个诊断需要用'|'分隔开
ageint年龄
sexstr性别
hrint心率
batchint选择生成ECG的数量

请求示例

curl -X POST "https://api.heartvoice.com.cn/api/v1/experimental/diffuSets" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-F "file=@normal_1-66b62bfc8acabc11cf5cc34cafe860f4.pt"
-d '{
"text": 'Sinus rhythm|Normal ECG.', # Clinical text report, multi-reports should be split by '|'
"age": 50, # Age of patient
"sex": 'M',
"hr": 80,
'batch': 1
}'

响应示例

{
"errorCode": "0",
"msg": "成功",
"data": {
"ECGTwin_imgs": [
[
0.0013988255523145199,
0.004246973432600498,
0.0010940954089164734,
0.004930938594043255,
-0.0024434151127934456,
0.00477356743067503,
...
]
]
}
}

可视化展示

import requests
import json
import base64
import io
from PIL import Image

def base64_to_image(base64_str):
# 解码Base64字符串
image_data = base64.b64decode(base64_str)
# 使用io.BytesIO将解码后的数据转换为文件对象
image_file = io.BytesIO(image_data)
# 使用PIL打开图片
image = Image.open(image_file)
return image

ecg_img = base64_to_image(ecg_data)
ecg_img.show()

文献

@article{lai2025ecgtwin,
title={ECGTwin: Personalized ECG Generation Using Controllable Diffusion Model},
author={Lai, Yongfan and Liu, Bo and Guan, Xinyan and Zhao, Qinghao and Li, Hongyan and Hong, Shenda},
journal={arXiv preprint arXiv:2508.02720},
year={2025}
}
Lai, Y., Liu, B., Guan, X., Zhao, Q., Li, H., & Hong, S. (2025). ECGTwin: Personalized ECG Generation Using Controllable Diffusion Model. arXiv preprint arXiv:2508.02720.

ECGFounder接口

POST /api/v1/experimental/ecgFounder

通过输入单导联信号可以生成十二导联信号。

请求参数

参数类型必填说明
ecgDataarrayECG信号数据数组(ADC值)
ecgSampleRatenumber采样率,单位Hz(推荐250-500)

请求示例

curl -X POST "https://api.heartvoice.com.cn/api/v1/experimental/ecgFounder" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"ecgData": [512, 515, 520, 518, 525, ...],
"ecgSampleRate": 500
}'

响应示例

{
"errorCode": "0",
"msg": "成功",
"data": {
"otherwise_normal_ecg": null,
"rsr_OR_QR_PATTERN_IN_V1_SUGGESTS_RIGHT_VENTRICULAR_CONDUCTION_DELAY": null,
"nonspecific_T_WAVE_ABNORMALITY_HAS_REPLACED_INVERTED_T_WAVES_IN": null,
"inverted_T_WAVES_HAVE_REPLACED_NON_SPECIFIC_T_WAVE_ABNORMALITY_IN": null,
"voltage_CRITERIA_FOR_LEFT_VENTRICULAR_HYPERTROPHY": null,
"bifascicular_BLOCK": null,
...
}
}

中英文对照

英文标签中文翻译
ABNORMAL ECG异常心电图
NORMAL SINUS RHYTHM正常窦性心律
NORMAL ECG正常心电图
SINUS RHYTHM窦性心律
SINUS BRADYCARDIA窦性心动过缓
ATRIAL FIBRILLATION心房颤动
SINUS TACHYCARDIA窦性心动过速
otherwise normal ecg其他方面正常的心电图
LEFT AXIS DEVIATION左轴偏移
PREMATURE VENTRICULAR COMPLEXES室性早搏
BORDERLINE ECG临界心电图
RIGHT BUNDLE BRANCH BLOCK右束支传导阻滞
SEPTAL INFARCT室间隔梗死
LEFT ATRIAL ENLARGEMENT左心房扩大
NONSPECIFIC T WAVE ABNORMALITY非特异性 T 波异常
LOW VOLTAGE QRS低电压 QRS 波群
PREMATURE ATRIAL COMPLEXES房性早搏
ANTERIOR INFARCT前壁梗死
INCOMPLETE RIGHT BUNDLE BRANCH BLOCK不完全性右束支传导阻滞
PREMATURE SUPRAVENTRICULAR COMPLEXES上室性早搏
LEFT BUNDLE BRANCH BLOCK左束支传导阻滞
NONSPECIFIC T WAVE ABNORMALITY NOW EVIDENT IN当前出现非特异性 T 波异常
NONSPECIFIC T WAVE ABNORMALITY NO LONGER EVIDENT IN非特异性 T 波异常已消失
T WAVE INVERSION NOW EVIDENT IN当前出现 T 波倒置
LATERAL INFARCT侧壁梗死
NONSPECIFIC ST ABNORMALITY非特异性 ST 段异常
LEFT VENTRICULAR HYPERTROPHY左心室肥大
T WAVE INVERSION NO LONGER EVIDENT INT 波倒置已消失
WITH RAPID VENTRICULAR RESPONSE伴快速心室反应
QT HAS SHORTENEDQT 间期缩短
QT HAS LENGTHENEDQT 间期延长
FUSION COMPLEXES融合波
ATRIAL FLUTTER心房扑动
MARKED SINUS BRADYCARDIA明显窦性心动过缓
WITH SINUS ARRHYTHMIA伴窦性心律不齐
NONSPECIFIC ST AND T WAVE ABNORMALITY非特异性 ST-T 波异常
LEFT ANTERIOR FASCICULAR BLOCK左前分支传导阻滞
RIGHT AXIS DEVIATION右轴偏移
ECTOPIC ATRIAL RHYTHM异位房性心律
UNDETERMINED RHYTHM未定型心律
ANTEROSEPTAL INFARCT前间隔梗死
RIGHTWARD AXIS右偏心电轴
ST NOW DEPRESSED IN当前出现 ST 段压低
WITH SHORT PR伴短 PR 间期
WITH MARKED SINUS ARRHYTHMIA伴明显窦性心律不齐
ST NO LONGER DEPRESSED INST 段压低已消失
INVERTED T WAVES HAVE REPLACED NONSPECIFIC T WAVE ABNORMALITY INT 波倒置取代了非特异性 T 波异常
NON-SPECIFIC CHANGE IN ST SEGMENT INST 段非特异性改变
NONSPECIFIC T WAVE ABNORMALITY HAS REPLACED INVERTED T WAVES IN非特异性 T 波异常取代了倒置 T 波
JUNCTIONAL RHYTHM交界性心律
ELECTRONIC ATRIAL PACEMAKER电子房起搏器
ABERRANT CONDUCTION异常传导
ELECTRONIC VENTRICULAR PACEMAKER电子心室起搏器
T WAVE INVERSION LESS EVIDENT INT 波倒置减轻
ANTEROLATERAL INFARCT前外侧梗死
WITH REPOLARIZATION ABNORMALITY伴复极异常
RSR' OR QR PATTERN IN V1 SUGGESTS RIGHT VENTRICULAR CONDUCTION DELAYV1 导联 RSR' 或 QR 波形提示右心室传导延迟
T WAVE INVERSION MORE EVIDENT INT 波倒置更加明显
WIDE QRS RHYTHM宽 QRS 波群心律
WITH PREMATURE VENTRICULAR OR ABERRANTLY CONDUCTED COMPLEXES伴室性早搏或异常传导复合波
RIGHT ATRIAL ENLARGEMENT右心房扩大
INFERIOR INFARCT下壁梗死
INCOMPLETE LEFT BUNDLE BRANCH BLOCK不完全性左束支传导阻滞
VOLTAGE CRITERIA FOR LEFT VENTRICULAR HYPERTROPHY左心室肥大的电压标准
OR DIGITALIS EFFECT或洋地黄效应
BIFASCICULAR BLOCK双分支传导阻滞
ST NO LONGER ELEVATED INST 段抬高已消失
WITH SLOW VENTRICULAR RESPONSE伴缓慢心室反应
ST ELEVATION NOW PRESENT IN当前出现 ST 段抬高
PREMATURE ECTOPIC COMPLEXES早期异位复合波
LEFT POSTERIOR FASCICULAR BLOCK左后分支传导阻滞
T WAVE AMPLITUDE HAS DECREASED INT 波振幅降低
WITH A COMPETING JUNCTIONAL PACEMAKER伴竞争性交界性起搏
RIGHT SUPERIOR AXIS DEVIATION右上轴偏移
BIATRIAL ENLARGEMENT双心房扩大
VENTRICULAR-PACED RHYTHM心室起搏心律
ATRIAL-PACED RHYTHM房起搏心律
T WAVE AMPLITUDE HAS INCREASED INT 波振幅增高
WITH QRS WIDENING伴 QRS 波群增宽
WITH 1ST DEGREE AV BLOCK伴一度房室传导阻滞
PROLONGED QTQT 间期延长
WITH PROLONGED AV CONDUCTION伴房室传导延迟
RIGHT VENTRICULAR HYPERTROPHY右心室肥大
WITH QRS WIDENING AND REPOLARIZATION ABNORMALITY伴 QRS 波群增宽及复极异常
ATRIAL-SENSED VENTRICULAR-PACED RHYTHM房感知-心室起搏心律
AV SEQUENTIAL OR DUAL CHAMBER ELECTRONIC PACEMAKER房室顺序或双腔电子起搏器
PULMONARY DISEASE PATTERN肺型心电图
ACUTE MI / STEMI急性心肌梗死 / ST 段抬高型心梗
INFERIOR-POSTERIOR INFARCT下后壁梗死
NONSPECIFIC INTRAVENTRICULAR CONDUCTION DELAY非特异性心室内传导延迟
PREMATURE VENTRICULAR AND FUSION COMPLEXES室性早搏及融合波
IN A PATTERN OF BIGEMINY二联律模式
AV DUAL-PACED RHYTHM双腔起搏心律
SUPRAVENTRICULAR TACHYCARDIA上室性心动过速
VENTRICULAR-PACED COMPLEXES心室起搏复合波
WIDE QRS TACHYCARDIA宽 QRS 心动过速
RSR' PATTERN IN V1V1 导联 RSR' 波形
ST LESS DEPRESSED INST 段压低减轻
VENTRICULAR TACHYCARDIA心室性心动过速
EARLY REPOLARIZATION早复极
ST MORE DEPRESSED INST 段压低加重
ANTEROLATERAL LEADS前外侧导联
ELECTRONIC DEMAND PACING电子按需起搏
RBBB AND LEFT ANTERIOR FASCICULAR BLOCK右束支阻滞伴左前分支阻滞
LATERAL INJURY PATTERN侧壁损伤图形
BIVENTRICULAR PACEMAKER DETECTED检测到双心室起搏器
SUSPECT UNSPECIFIED PACEMAKER FAILURE疑似未明确的起搏器功能障碍
WOLFF-PARKINSON-WHITE预激综合征(Wolff-Parkinson-White 综合征)
WITH VENTRICULAR ESCAPE COMPLEXES伴心室逸搏复合波
INFERIOR INJURY PATTERN下壁损伤图形
CONSIDER RIGHT VENTRICULAR INVOLVEMENT IN ACUTE INFERIOR INFARCT急性下壁心梗伴右室受累可能
ST ELEVATION HAS REPLACED ST DEPRESSION INST 段抬高取代 ST 段压低
NONSPECIFIC INTRAVENTRICULAR BLOCK非特异性室内传导阻滞
MASKED BY FASCICULAR BLOCK被分支阻滞掩盖
PEDIATRIC ECG ANALYSIS儿科心电图分析
BLOCKED阻滞
WITH UNDETERMINED RHYTHM IRREGULARITY伴不确定心律不齐
LEFTWARD AXIS左偏电轴
WITH 2ND DEGREE SA BLOCK MOBITZ I伴二度窦房阻滞 Mobitz I 型
ACUTE急性
ABNORMAL LEFT AXIS DEVIATION异常左轴偏移
WITH COMPLETE HEART BLOCK伴完全性房室传导阻滞
NO P-WAVES FOUND未检测到 P 波
ST LESS ELEVATED INST 段抬高减轻
WITH RETROGRADE CONDUCTION伴逆向传导
ST MORE ELEVATED INST 段抬高加重
JUNCTIONAL BRADYCARDIA交界性心动过缓
WITH VARIABLE AV BLOCK伴变异性房室传导阻滞
ANTERIOR INJURY PATTERN前壁损伤图形
WITH JUNCTIONAL ESCAPE COMPLEXES伴交界性逸搏复合波
ACUTE MI急性心肌梗死
ACUTE PERICARDITIS急性心包炎
POSTERIOR INFARCT后壁梗死
IDIOVENTRICULAR RHYTHM特发性心室心律
WITH 2ND DEGREE SA BLOCK MOBITZ II伴二度窦房阻滞 Mobitz II 型
R IN AVLAVL 导联 R 波
SINUS/ATRIAL CAPTURE窦性/房性捕获
AV DUAL-PACED COMPLEXES双腔起搏复合波
INFEROLATERAL INJURY PATTERN下外侧损伤图形
RBBB AND LEFT POSTERIOR FASCICULAR BLOCK右束支阻滞伴左后分支阻滞
ANTEROLATERAL INJURY PATTERN前外侧损伤图形
ATRIAL-PACED COMPLEXES房起搏复合波
WITH SINUS PAUSE伴窦性停搏
BIVENTRICULAR HYPERTROPHY双心室肥大
ABNORMAL RIGHT AXIS DEVIATION异常右轴偏移
SUPRAVENTRICULAR COMPLEXES上室性复合波
WITH 2ND DEGREE AV BLOCK MOBITZ I伴二度房室传导阻滞 Mobitz I 型
WITH 2:1 AV CONDUCTION伴 2:1 房室传导
WITH AV DISSOCIATION伴房室分离
MULTIFOCAL ATRIAL TACHYCARDIA多源性房性心动过速

文献

@article{li2025electrocardiogram,
title={An Electrocardiogram Foundation Model Built on over 10 Million Recordings},
author={Li, Jun and Aguirre, Aaron D and Junior, Valdery Moura and Jin, Jiarui and Liu, Che and Zhong, Lanhai and Sun, Chenxi and Clifford, Gari and Brandon Westover, M and Hong, Shenda},
journal={NEJM AI},
volume={2},
number={7},
pages={AIoa2401033},
year={2025},
publisher={Massachusetts Medical Society}
}
Li, J., Aguirre, A., Moura, J., Liu, C., Zhong, L., Sun, C., ... & Hong, S. (2024). An electrocardiogram foundation model built on over 10 million recordings with external evaluation across multiple domains. arXiv preprint arXiv:2410.04133.