ECG-X: Making ECGs explainable with colour to support early detection of life-threatening heart conditions

“在一家忙碌的医院里，一位初级急症室医生正在学习解读心电图。以前她会数小方块，并试图使用三角法手动确定波的开始和结束-例如在纸上画切线-而不是在iPad上探索数据，改变ECG的轴和方向，将颜色应用于曲线下的区域并放大以了解信号的细节。她用来理解数据的可视化工具也以另一种方式支持她：提供她正在调查的条件的可能性的自动估计，用自然语言解释它是如何得出判断的。由于自动化解释使用“认知拟合”-人类和机器共享相同的数据表示-它直观易懂，易于检查和探索。在这种情况下，通过ECG数据的视觉呈现来提供拟合。支持人工判读的技术也被用作机器判读的基础，该技术将联合收割机预先注意处理与临床知识相结合，以突出信号中的异常，从而确保可靠性和准确性。”“一个病人开始服用一种治疗癌症的新药。他没有注意到它会引起任何副作用，但他的智能手表提醒他，他的心脏的电活动可能已经改变，并建议他紧急咨询临床医生。他在一次紧急会诊中向医生展示了这些数据，并改变了他的药物。他的心脏活动很快恢复正常。“上述情况显示了我们的研究旨在实现的转变-从专家难以手动解释ECG，到在家自我监测以检测可能导致心脏猝死的条件。心电图（ECG）是心脏电活动的图形表示，广泛用于临床实践中检测心脏病理。众所周知，ECG解释是复杂的，对人类和机器都具有挑战性。这项研究将开发一套可视化技术，用于询问ECG数据，将视觉感知原理与临床知识相结合，以创建决策支持工具，其中人类和机器共享相同的数据表示。该研究将分为两个部分：第一部分，可视化技术将与临床医生共同创建，以开发在临床实践中值得信赖的可靠工具，并支持可用作操纵ECG数据以辅助解释的基础理论，并支持自动化解释算法。第二部分，将与公众一起试验和进一步开发特定条件下的选定数据呈现形式，以确定这些技术是否可能被外行人用于监测自己的心脏健康。我们的长期愿景是设计出临床上可靠且可解释的类人AI，使患者或其护理人员能够直观地自我监测心电图，以了解临床环境之外可能危及生命的心脏状况。这一愿景的最终目标是促进心脏性猝死的一级和二级预防--心脏性猝死是一种灾难性事件，占心血管死亡率的50%，每年在美国造成约30万人死亡，在英国造成6万人死亡。导致心脏性猝死的心脏电气问题通常只能在心电图上检测到，缺血性心脏病的早期症状可以在其他主要症状发生之前在心电图上检测到。因此，改善心电图解释对于早期检测潜在的致命心脏病至关重要。更快的诊断和自我监测的能力将特别有利于妇女，由于与男性不同的症状表现，她们往往会延迟治疗。

"In a busy hospital, a junior A&E doctor is learning to interpret ECGs. Where once she would have been counting tiny squares and trying to manually determine the beginning and end of waves using trigonometry - for example drawing tangents - on a paper printout, instead she is exploring the data on an iPad, changing the axes and orientation of the ECG, applying colour to the area under the curve and zooming in to understand details of the signal. The visualisation tool she uses to understand the data also supports her in another way: providing an automated estimation of the likelihood of the conditions she is investigating, explaining in natural language how it has come to its judgement. Because the automated interpretation uses 'cognitive fit' - where the human and the machine share the same representation of the data - it is intuitively understandable, and easy to check and explore. In this case, the fit is provided by the visual presentation of the ECG data. The techniques that support human interpretation, which combine pre-attentive processing to highlight anomalies in the signal with clinical knowledge to ensure reliability and accuracy, are also used as the basis for the machine interpretation.""A patient has started taking a new medication for cancer treatment. He hasn't noticed it causing any side effects, but his smart watch has alerted him that the electrical activity of his heart may have changed, and advises him to consult a clinician urgently. He shows the data to his doctor in an emergency consultation, and has his medication changed. His heart activity soon returns to normal."The scenarios above show the transformation our research aims to achieve - moving from difficult manual interpretation of ECGs by experts, to self-monitoring at home to detect conditions that may lead to sudden cardiac death.The Electrocardiogram (ECG) is a graphical representation of the heart's electrical activity that is widely used in clinical practice for detecting cardiac pathologies. ECG interpretation is known to be complex, challenging both humans and machines. This research will develop a suite of visualisation techniques for interrogating ECG data, combining principles of visual perception with clinical knowledge to create decision support tools where humans and machines share the same representation of the data. The research will have two strands:In the first, visualisation techniques will be co-created with clinicians to develop reliable tools that will be trusted in clinical practice, and underpinning theory that can be used as a foundation for manipulating ECG data to assist interpretation, and support automated interpretation algorithms.In the second, selected forms of data presentation for specific conditions will be trialled and further developed with members of the public, to determine whether the techniques could potentially be used by lay people for monitoring their own cardiac health. Our long-term vision is to engineer clinically reliable and explainable human-like AI that will empower patients or their caregivers to intuitively self-monitor their ECGs for potentially life-threatening cardiac conditions outside the clinical setting. This vision ultimately aims to promote primary and secondary prevention of sudden cardiac death - a catastrophic event accounting for 50% of cardiovascular mortality, causing an estimated 300,000 deaths in the US and 60,000 deaths in the UK annually. Electrical problems with the heart leading to sudden cardiac death are often detectable only on an ECG, and the early signs of ischaemic heart disease can be detected on an ECG before other major symptoms occur. Improving ECG interpretation is thus essential for the earlier detection of potentially lethal heart conditions. Faster diagnosis and the ability to self-monitor will particularly benefit women, who often experience a delay in treatment due to different symptom presentation to men.