Paskaranandavadivel Niranchan, Du Peng, Erickson Jonathan, O'Grady Gregory, Cheng Leo K
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:1938-41. doi: 10.1109/EMBC.2015.7318763.
Gastric contractions are governed by a bioelectrical event known as slow waves. High-resolution electrical mapping has recently been applied to study complex gastric slow wave spatiotemporal propagations in detail. As these methods are translated to clinical and experimental applications, it is evident that efficient and automated methods are a necessity for analysis. Despite automated methods to detect slow wave events, manual review and correction remains necessary due to the presence of experimental noise in the recordings. Manual deletion of invalid slow wave events is time consuming and inefficient. We have therefore developed an algorithm to eliminate invalid markers of slow waves, via the use of frequency and morphological analysis. The techniques were validated with experimental data using serosal gastric slow wave recordings from animals and humans with a sensitivity of 90% and specificity of 85%. It is anticipated these methods will facilitate analyzing high-resolution slow wave mapping data and accelerate clinical translation of electrical mapping to clinical and diagnostic gastroentrology.
胃收缩受一种称为慢波的生物电活动支配。高分辨率电标测最近已被用于详细研究复杂的胃慢波时空传播。随着这些方法被转化为临床和实验应用，显然高效且自动化的方法对于分析是必要的。尽管有自动检测慢波事件的方法，但由于记录中存在实验噪声，人工审查和校正仍然是必要的。手动删除无效的慢波事件既耗时又低效。因此，我们开发了一种算法，通过使用频率和形态分析来消除慢波的无效标记。这些技术通过使用来自动物和人类的胃浆膜慢波记录的实验数据进行了验证，灵敏度为90%，特异性为85%。预计这些方法将有助于分析高分辨率慢波标测数据，并加速电标测在临床和诊断胃肠病学中的临床转化。
